\ud83d\udc41\ufe0f THE COMPLETE 2026 PROJECT LIBRARY<\/p>\n
100 Computer Vision Projects
\nFrom OpenCV Basics to Generative AI<\/span><\/h1>\nHand-picked project ideas with datasets, tools and difficulty levels \u2014 covering image classification, object detection, OCR, medical imaging, self-driving perception and image generation.<\/p>\n<\/div>\n
![\"Computer](\"https:\/\/1000sciencefairprojects.com\/tech\/wp-content\/uploads\/2026\/06\/computer-vision-projects-animated.gif\")
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Object detection in action \u2014 the skill behind project ideas #21 to #30 below.<\/p>\n
\n\u26a1 Quick Answer<\/p>\n
Computer vision projects<\/strong> teach machines to understand images and video. The best learning path in 2026 is: classical OpenCV projects<\/strong> (edge detection, face detection, document scanning) \u2192 CNN classification<\/strong> (cats vs dogs, transfer learning) \u2192 detection and tracking<\/strong> (YOLO, DeepSORT) \u2192 specialized domains<\/strong> (OCR, medical imaging, self-driving) \u2192 generative and multi-modal AI<\/strong> (Stable Diffusion, CLIP, vision-language models). This page lists all 100 ideas with descriptions, tools and datasets.<\/p>\n<\/div>\nComputer vision is the most visible<\/em> branch of AI \u2014 literally. When your phone unlocks with your face, when a car brakes for a pedestrian, when a hospital flags a suspicious X-ray, that’s vision code making a judgment in milliseconds. And here’s the good news: every one of those systems started as someone’s first OpenCV script<\/strong>.<\/p>\nWe organized these 100 computer vision project ideas<\/strong> into ten career-aligned, color-coded categories. Start with the green beginner zone \u2014 no GPU required \u2014 then follow your interests into detection, medical AI, autonomous driving or generative imagery. Each idea tells you what you will build and exactly what it teaches.<\/p>\n\ud83d\udcda What’s Inside: All 10 Categories at a Glance<\/h2>\n
\n\n\nCategory<\/th>\n Projects<\/th>\n Level<\/th>\n Key Tools<\/th>\n<\/tr>\n<\/thead>\n \n\n\ud83c\udf31 Beginner Computer Vision Projects<\/a><\/td>\n 1\u201310<\/td>\n Beginner<\/td>\n OpenCV, NumPy<\/td>\n<\/tr>\n \n\ud83d\uddbc\ufe0f Image Classification Projects<\/a><\/td>\n 11\u201320<\/td>\n Beginner+<\/td>\n CNNs, Transfer Learning, ViT<\/td>\n<\/tr>\n \n\ud83c\udfaf Object Detection & Tracking Projects<\/a><\/td>\n 21\u201330<\/td>\n Intermediate<\/td>\n YOLO, DeepSORT, MediaPipe<\/td>\n<\/tr>\n \n\ud83d\ude42 Face Analysis & Recognition Projects<\/a><\/td>\n 31\u201340<\/td>\n Intermediate<\/td>\n face_recognition, MediaPipe<\/td>\n<\/tr>\n \n\ud83d\udcc4 OCR & Document Analysis Projects<\/a><\/td>\n 41\u201350<\/td>\n Intermediate<\/td>\n Tesseract, EasyOCR, LayoutLM<\/td>\n<\/tr>\n \n\ud83e\ude7a Medical & Scientific Imaging Projects<\/a><\/td>\n 51\u201360<\/td>\n Advanced<\/td>\n U-Net, Grad-CAM, PyTorch<\/td>\n<\/tr>\n \n\ud83c\udf3e Agriculture & Environment Projects<\/a><\/td>\n 61\u201370<\/td>\n Intermediate<\/td>\n CNNs, Drone & Satellite Data<\/td>\n<\/tr>\n \n\ud83d\ude97 Autonomous Vehicles & Robotics Projects<\/a><\/td>\n 71\u201380<\/td>\n Advanced<\/td>\n Segmentation, Depth, SLAM<\/td>\n<\/tr>\n \n\ud83c\udfa8 Generative AI & Image Synthesis Projects<\/a><\/td>\n 81\u201390<\/td>\n Advanced<\/td>\n GANs, Stable Diffusion, LoRA<\/td>\n<\/tr>\n \n\ud83d\ude80 Advanced & Real-Time Vision Systems<\/a><\/td>\n 91\u2013100<\/td>\n Expert<\/td>\n CLIP, NeRF, ONNX, MLOps<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\nTap any category to jump straight to its project list.<\/p>\n
\ud83c\udf31 1. Beginner Computer Vision Projects (Projects 1\u201310)<\/span><\/h2>\n1<\/span>Handwritten Digit Recognition (MNIST)<\/h3>\nThe ‘Hello World’ of computer vision – train a simple convolutional neural network to read handwritten digits with 99% accuracy. You will learn image tensors, convolutions, and the full training loop in TensorFlow or PyTorch.<\/p>\n
2<\/span>Image Color Detector<\/h3>\nBuild a tool that identifies the dominant colors in any photo using K-Means clustering on pixel values. A gentle introduction to treating images as data with OpenCV and NumPy.<\/p>\n
3<\/span>Edge Detection Playground<\/h3>\nApply Canny, Sobel, and Laplacian edge detectors to your own photos and compare results side by side. Understand gradients and filters – the mathematical heart of classical vision.<\/p>\n
4<\/span>Pencil Sketch & Cartoon Filter App<\/h3>\nConvert photos into pencil sketches and cartoon-style art using Gaussian blurring, bilateral filtering, and edge masks. Instant visual gratification while learning image transformations.<\/p>\n
5<\/span>Face Detection with Haar Cascades<\/h3>\nDetect faces in photos and webcam feeds with OpenCV’s classic Haar cascade classifiers. The perfect first step before modern deep learning detectors.<\/p>\n
6<\/span>QR Code and Barcode Scanner<\/h3>\nBuild a scanner that reads QR codes and barcodes from images or live camera input using OpenCV and pyzbar. A practical project you will actually use.<\/p>\n
7<\/span>Image Stitching Panorama Maker<\/h3>\nStitch overlapping photos into a seamless panorama using feature matching with SIFT\/ORB and homography transforms. Learn how your phone’s panorama mode really works.<\/p>\n
8<\/span>Motion Detection Security Camera<\/h3>\nTurn a webcam into a motion-triggered security camera using frame differencing and contour detection. Save snapshots whenever movement is detected.<\/p>\n
9<\/span>Green Screen Background Replacement<\/h3>\nImplement chroma keying that swaps a green background for any image using HSV color masking. The foundation of every video call background filter.<\/p>\n
10<\/span>Document Photo Scanner<\/h3>\nDetect a document’s edges in a photo, correct the perspective, and output a clean scanned-look image. A mini CamScanner built with contour detection and warp transforms.<\/p>\n
\ud83d\uddbc\ufe0f 2. Image Classification Projects (Projects 11\u201320)<\/span><\/h2>\n11<\/span>Cats vs Dogs Classifier<\/h3>\nThe classic binary classification challenge – train a CNN to distinguish cats from dogs on 25,000 images. Learn data augmentation, dropout, and how to fight overfitting.<\/p>\n
12<\/span>CIFAR-10 Object Classification<\/h3>\nClassify tiny 32×32 images into ten everyday categories like planes, ships, and trucks. A standard benchmark that teaches you to squeeze accuracy from limited resolution.<\/p>\n
13<\/span>Transfer Learning with Pretrained Models<\/h3>\nFine-tune ResNet, EfficientNet, or MobileNet on your own small dataset and watch accuracy jump with minimal training. The single most practical skill in applied vision.<\/p>\n
14<\/span>Food-101 Dish Recognition<\/h3>\nIdentify 101 food dishes from photographs – from samosas to sushi. A deliciously challenging dataset with real-world lighting and plating variety.<\/p>\n
15<\/span>Flower Species Classifier<\/h3>\nClassify 102 flower species from the Oxford Flowers dataset using fine-tuned CNNs. Beautiful data and a genuine fine-grained classification challenge.<\/p>\n
16<\/span>Garbage Classification for Recycling<\/h3>\nSort waste images into glass, plastic, metal, paper, and organic categories. An environmental AI project with obvious smart-city applications.<\/p>\n
17<\/span>Fashion Product Categorization<\/h3>\nClassify clothing images into categories and attributes using the Fashion-MNIST and DeepFashion datasets. The technology behind visual search in e-commerce.<\/p>\n
18<\/span>Indian Currency Note Recognition<\/h3>\nRecognize rupee note denominations from photos – a genuinely useful accessibility tool for visually impaired users when paired with text-to-speech.<\/p>\n
19<\/span>Sports Action Classification<\/h3>\nClassify images of cricket shots, football kicks, or yoga poses into action categories. Combine pose estimation features with CNN classifiers for higher accuracy.<\/p>\n
20<\/span>Vision Transformer (ViT) vs CNN Showdown<\/h3>\nTrain a Vision Transformer and a CNN on the same dataset and benchmark accuracy, speed, and data hunger. Understand why transformers are taking over vision.<\/p>\n
\ud83c\udfaf 3. Object Detection & Tracking Projects (Projects 21\u201330)<\/span><\/h2>\n21<\/span>Real-Time Object Detection with YOLO<\/h3>\nRun YOLOv8\/YOLOv9 to detect and label people, cars, and animals in live video at 30+ FPS. Learn bounding boxes, confidence thresholds, and non-max suppression.<\/p>\n
22<\/span>Custom Object Detector for Your Own Dataset<\/h3>\nLabel your own images with Roboflow or LabelImg and train YOLO to detect anything – your pet, a product, a logo. The complete custom detection pipeline.<\/p>\n
23<\/span>Vehicle Counting on Highway Footage<\/h3>\nCount cars, buses, and trucks crossing a virtual line in traffic videos using detection plus centroid tracking. Real transportation analytics in action.<\/p>\n
24<\/span>Multi-Object Tracking with DeepSORT<\/h3>\nTrack multiple people across video frames with persistent IDs by combining YOLO detection and DeepSORT appearance embeddings. The backbone of retail and security analytics.<\/p>\n
25<\/span>Helmet Detection for Road Safety<\/h3>\nDetect whether two-wheeler riders are wearing helmets in traffic footage. A high-impact project aligned with road safety enforcement in India.<\/p>\n
26<\/span>Social Distancing Monitor<\/h3>\nMeasure distances between detected people in CCTV-style video and flag violations using camera calibration and bird’s-eye-view transforms.<\/p>\n
27<\/span>Wildlife Camera Trap Analyzer<\/h3>\nAutomatically identify animal species in camera-trap images to support conservation research. Handle night-vision images, motion blur, and severe class imbalance.<\/p>\n
28<\/span>Drone-View Object Detection<\/h3>\nDetect tiny objects – people, vehicles, boats – in aerial drone imagery using the VisDrone dataset. Learn why small-object detection needs special techniques.<\/p>\n
29<\/span>Hand Tracking and Gesture Control<\/h3>\nTrack 21 hand landmarks in real time with MediaPipe and control your computer’s volume or slides with gestures. Instant futuristic demo material.<\/p>\n
30<\/span>Ball Tracking for Sports Analytics<\/h3>\nTrack a cricket or tennis ball across video frames, plot its trajectory, and estimate speed. The DIY version of broadcast Hawk-Eye graphics.<\/p>\n
\ud83d\ude42 4. Face Analysis & Recognition Projects (Projects 31\u201340)<\/span><\/h2>\n31<\/span>Face Recognition Attendance System<\/h3>\nBuild an attendance logger that recognizes registered faces from a webcam and records entries with timestamps using the face_recognition library and OpenCV.<\/p>\n
32<\/span>Facial Emotion Detection<\/h3>\nClassify faces into emotions – happy, sad, angry, surprised – using CNNs trained on the FER-2013 dataset. Discuss honestly where emotion AI works and where it fails.<\/p>\n
33<\/span>Age and Gender Estimation<\/h3>\nPredict approximate age range and gender from face images using pretrained deep models. A study in model bias and responsible reporting of uncertain predictions.<\/p>\n
34<\/span>Drowsiness Detection for Drivers<\/h3>\nMonitor eye aspect ratio and head pose through a dashcam-style feed and sound an alarm when a driver’s eyes stay closed too long. Vision AI that can save lives.<\/p>\n
35<\/span>Face Mask Detection<\/h3>\nDetect whether people are wearing masks using transfer learning with MobileNet. A pandemic-era classic that still teaches real-time classification deployment.<\/p>\n
36<\/span>Virtual Makeup and Filter App<\/h3>\nApply lipstick, sunglasses, or fun filters that track facial landmarks in real time – the Snapchat effect built with MediaPipe Face Mesh’s 468 landmarks.<\/p>\n
37<\/span>Face Blurring for Privacy Protection<\/h3>\nAutomatically detect and blur all faces in photos and videos – the privacy tool every news organization and dashcam company needs.<\/p>\n
38<\/span>Smile-Triggered Selfie Camera<\/h3>\nCapture a photo automatically when everyone in frame is smiling, using facial landmark geometry. A delightful beginner-friendly face project.<\/p>\n
39<\/span>Head Pose Estimation<\/h3>\nEstimate where a person is looking by computing head yaw, pitch, and roll from facial landmarks. Used in driver monitoring and attention analytics.<\/p>\n
40<\/span>Deepfake Detection Basics<\/h3>\nTrain a classifier to spot AI-generated face images using artifacts in frequency space and facial inconsistencies. Join the defense side of the deepfake arms race.<\/p>\n
\ud83d\udcc4 5. OCR & Document Analysis Projects (Projects 41\u201350)<\/span><\/h2>\n41<\/span>Receipt Scanner and Expense Tracker<\/h3>\nExtract merchant, date, and total from receipt photos with Tesseract or EasyOCR, then log expenses to a spreadsheet automatically. Practical OCR with instant personal value.<\/p>\n
42<\/span>License Plate Recognition (ANPR)<\/h3>\nDetect vehicle number plates and read their text by chaining object detection with OCR. Mirrors real automatic number plate recognition systems used in parking and tolling.<\/p>\n
43<\/span>Handwriting Recognition<\/h3>\nGo beyond digits – recognize full handwritten words and sentences using CRNN or TrOCR transformer models on the IAM handwriting dataset.<\/p>\n
44<\/span>Invoice Data Extraction Pipeline<\/h3>\nPull structured fields – invoice number, GST, line items, totals – from PDF invoices into JSON using layout-aware models like LayoutLM. High-value business automation.<\/p>\n
45<\/span>Math Equation Solver from Photos<\/h3>\nRecognize a handwritten or printed equation from a photo and solve it with SymPy. The Photomath concept, built end to end.<\/p>\n
46<\/span>Multi-Language Sign Board Reader<\/h3>\nDetect and read text from street signs in multiple Indian languages and scripts, then translate it. Combines scene-text detection (EAST\/CRAFT) with OCR and translation APIs.<\/p>\n
47<\/span>Old Document Digitization & Restoration<\/h3>\nClean noisy scans of old books with binarization and denoising, then OCR them into searchable text. Cultural preservation meets image processing.<\/p>\n
48<\/span>Table Extraction from Documents<\/h3>\nDetect tables in scanned reports and reconstruct them as CSV files, preserving rows and columns. One of the hardest and most requested document AI tasks.<\/p>\n
49<\/span>Business Card Reader App<\/h3>\nExtract names, phone numbers, and emails from business card photos straight into a contacts file using OCR plus regex and NER post-processing.<\/p>\n
50<\/span>Answer Sheet \/ OMR Evaluator<\/h3>\nAutomatically grade multiple-choice OMR sheets from photos using circle detection and thresholding. Every coaching institute wants exactly this tool.<\/p>\n
\ud83e\ude7a 6. Medical & Scientific Imaging Projects (Projects 51\u201360)<\/span><\/h2>\n51<\/span>Pneumonia Detection from Chest X-Rays<\/h3>\nTrain a CNN to detect pneumonia in chest radiographs and visualize its reasoning with Grad-CAM heatmaps. The canonical introduction to responsible medical imaging AI.<\/p>\n
52<\/span>Skin Lesion Classification<\/h3>\nClassify dermatoscopic images into lesion types using the HAM10000 dataset. Learn about class imbalance, calibration, and why dermatology AI needs careful validation.<\/p>\n
53<\/span>Brain Tumor Segmentation from MRI<\/h3>\nSegment tumor regions in MRI scans with a U-Net architecture. Pixel-level prediction – one of the most respected skills in medical vision.<\/p>\n
54<\/span>Blood Cell Counting and Classification<\/h3>\nCount and classify red cells, white cells, and platelets in microscope images. Automating one of pathology’s most repetitive tasks.<\/p>\n
55<\/span>Diabetic Retinopathy Grading<\/h3>\nGrade retinal fundus images by disease severity using the APTOS dataset. A Kaggle-famous challenge with massive screening impact in India.<\/p>\n
56<\/span>Malaria Parasite Detection in Cell Images<\/h3>\nDetect malaria-infected cells in thin blood smear images with a lightweight CNN that could run on a low-cost field microscope.<\/p>\n
57<\/span>Bone Fracture Detection in X-Rays<\/h3>\nDetect and localize fractures in musculoskeletal X-rays using the MURA dataset. Learn how radiologist-AI collaboration is actually evaluated.<\/p>\n
58<\/span>COVID-19 CT Scan Analysis<\/h3>\nClassify and segment lung abnormalities in CT slices – and study the published pitfalls of pandemic-era models as a masterclass in dataset bias.<\/p>\n
59<\/span>Microscopy Cell Segmentation<\/h3>\nSegment individual cells in crowded microscopy images using Cellpose or StarDist. A workhorse task across all of biological research.<\/p>\n
60<\/span>Plant Cell vs Animal Cell Classifier<\/h3>\nA science-fair friendly project classifying microscope slide images of plant and animal cells, with clear visual explanations of what features the model uses.<\/p>\n
\ud83c\udf3e 7. Agriculture & Environment Projects (Projects 61\u201370)<\/span><\/h2>\n61<\/span>Plant Disease Detection from Leaf Images<\/h3>\nClassify crop diseases from leaf photos using the PlantVillage dataset – hugely relevant for Indian agritech, deployable as a farmer-facing mobile app.<\/p>\n
62<\/span>Weed vs Crop Detection for Smart Farming<\/h3>\nDetect weeds among crop rows in field images to enable targeted spraying. Precision agriculture that cuts herbicide use dramatically.<\/p>\n
63<\/span>Fruit Ripeness Grading<\/h3>\nGrade tomatoes, bananas, or mangoes by ripeness from color and texture features. Straightforward, visual, and directly useful in supply chains.<\/p>\n
64<\/span>Crop Yield Estimation from Drone Imagery<\/h3>\nCount plants and estimate yield from aerial farm images using detection and density-map regression. Agritech startups are built on exactly this capability.<\/p>\n
65<\/span>Satellite Image Land Cover Classification<\/h3>\nClassify satellite tiles into forest, water, urban, and farmland using EuroSAT data. Your gateway into remote sensing and geospatial deep learning.<\/p>\n
66<\/span>Deforestation Change Detection<\/h3>\nCompare satellite images across years to automatically flag deforestation zones. Environmental monitoring with before\/after change-detection networks.<\/p>\n
67<\/span>Cattle and Livestock Counting<\/h3>\nCount and monitor livestock in drone or CCTV footage of farms and pastures. Animal detection with occlusion challenges that sharpen real skills.<\/p>\n
68<\/span>Fish Species Identification<\/h3>\nIdentify fish species from photos for fisheries monitoring and biodiversity studies – useful from aquariums to research vessels.<\/p>\n
69<\/span>Forest Fire and Smoke Detection<\/h3>\nDetect smoke plumes and fire in camera and satellite imagery for early-warning systems. Latency and false-alarm trade-offs make this a serious engineering exercise.<\/p>\n
70<\/span>Soil Type Classification from Images<\/h3>\nClassify soil types from close-up photographs using texture analysis and CNNs, helping farmers pick suitable crops without lab testing.<\/p>\n
\ud83d\ude97 8. Autonomous Vehicles & Robotics Projects (Projects 71\u201380)<\/span><\/h2>\n71<\/span>Lane Detection for Self-Driving Cars<\/h3>\nDetect lane lines in dashcam video using Hough transforms, then upgrade to deep segmentation models. The classic first step into autonomous driving.<\/p>\n
72<\/span>Traffic Sign Recognition<\/h3>\nClassify 43 categories of road signs from the German GTSRB benchmark – a core perception module in every driver-assistance system.<\/p>\n
73<\/span>Traffic Light State Detection<\/h3>\nDetect traffic lights and classify their state (red\/yellow\/green) in street scenes. Small objects, high stakes, real-world messiness.<\/p>\n
74<\/span>Pedestrian Detection and Intent Estimation<\/h3>\nDetect pedestrians and estimate whether they are about to cross using pose cues. The hardest, most safety-critical perception problem on the road.<\/p>\n
75<\/span>Monocular Depth Estimation<\/h3>\nPredict a depth map from a single camera image using models like MiDaS. Understand how 2D pixels become 3D understanding without LiDAR.<\/p>\n
76<\/span>Road Pothole Detection<\/h3>\nDetect potholes in dashcam footage and map them with GPS – a civic-tech project with obvious value for Indian municipal authorities.<\/p>\n
77<\/span>Semantic Segmentation of Street Scenes<\/h3>\nLabel every pixel of driving scenes as road, vehicle, person, or building using DeepLab or SegFormer on the Cityscapes dataset.<\/p>\n
78<\/span>Visual Odometry from Camera Motion<\/h3>\nEstimate a vehicle’s trajectory purely from camera frames using feature tracking and pose estimation. The vision half of SLAM.<\/p>\n
79<\/span>Line-Following Robot with a Camera<\/h3>\nBuild a Raspberry Pi robot that follows a track using camera input instead of IR sensors. Embedded vision and control on a student budget.<\/p>\n
80<\/span>Parking Space Occupancy Detection<\/h3>\nDetect free and occupied parking slots from an overhead camera feed – a smart-city project deployable with one cheap camera per lot.<\/p>\n
\ud83c\udfa8 9. Generative AI & Image Synthesis Projects (Projects 81\u201390)<\/span><\/h2>\n81<\/span>Photo Colorization of Black & White Images<\/h3>\nBring old family photos to life with pretrained colorization networks – visually stunning results that make portfolios memorable.<\/p>\n
82<\/span>Neural Style Transfer Art Generator<\/h3>\nRepaint your photos in the style of famous art movements by optimizing content and style losses. The project that made deep learning visually famous.<\/p>\n
83<\/span>GAN for Face Generation<\/h3>\nTrain a DCGAN or StyleGAN to generate realistic faces and explore latent-space arithmetic. Study mode collapse and training instability firsthand.<\/p>\n
84<\/span>Image Super-Resolution Enhancer<\/h3>\nUpscale low-resolution images 4x with ESRGAN or SwinIR, restoring crisp detail. The ‘enhance’ button from crime dramas, actually built.<\/p>\n
85<\/span>Stable Diffusion Fine-Tuning with LoRA<\/h3>\nFine-tune a diffusion model on your own art style or product photos using LoRA adapters. The hottest generative-vision skill of 2026.<\/p>\n
86<\/span>AI Image Inpainting Object Remover<\/h3>\nErase unwanted objects or people from photos and let a diffusion model fill the gap convincingly. The magic-eraser feature, demystified.<\/p>\n
87<\/span>Text-to-Image Prompt Engineering Lab<\/h3>\nSystematically study how prompt wording, negative prompts, and guidance scales change diffusion outputs, and document your findings like a scientist.<\/p>\n
88<\/span>Image-to-Image Sketch to Photo Translation<\/h3>\nTranslate sketches into photorealistic images with Pix2Pix or ControlNet. Conditional generation that designers genuinely use.<\/p>\n
89<\/span>Old Photo Restoration Pipeline<\/h3>\nCombine scratch removal, denoising, face enhancement, and colorization into one restoration pipeline for damaged vintage photographs.<\/p>\n
90<\/span>AI-Generated Image Detector<\/h3>\nTrain a classifier to distinguish real photos from diffusion-generated images – the counterpart to generative AI that newsrooms and platforms urgently need.<\/p>\n
\ud83d\ude80 10. Advanced & Real-Time Vision Systems (Projects 91\u2013100)<\/span><\/h2>\n91<\/span>Real-Time Pose Estimation Fitness Coach<\/h3>\nCount squats and push-ups and correct posture in real time using MediaPipe or MoveNet pose landmarks. A complete AI fitness product in one project.<\/p>\n
92<\/span>Visual Question Answering System<\/h3>\nBuild a system that answers natural-language questions about images using vision-language models like BLIP or LLaVA. Where vision meets LLMs.<\/p>\n
93<\/span>CLIP-Powered Semantic Image Search<\/h3>\nSearch your photo library with text queries like ‘sunset over water’ using CLIP embeddings and a vector database. Multi-modal retrieval done right.<\/p>\n
94<\/span>Image Captioning with Transformers<\/h3>\nGenerate natural-language captions for photos by pairing a vision encoder with a language decoder – a showcase bridge between two AI worlds.<\/p>\n
95<\/span>3D Reconstruction with NeRF \/ Gaussian Splatting<\/h3>\nTurn a set of phone photos into an explorable 3D scene using Neural Radiance Fields or 3D Gaussian Splatting – the frontier of 3D vision.<\/p>\n
96<\/span>Edge AI Vision on Raspberry Pi<\/h3>\nDeploy quantized detection models on a Raspberry Pi or Jetson Nano and measure FPS, power, and accuracy trade-offs. Real-world deployment engineering.<\/p>\n
97<\/span>Video Action Recognition<\/h3>\nClassify human actions – cooking, dancing, cricket batting – in video clips using 3D CNNs or video transformers on the Kinetics dataset.<\/p>\n
98<\/span>Anomaly Detection for Factory Quality Control<\/h3>\n
<\/p>\n\u26a1 Quick Answer<\/p>\n
Computer vision projects<\/strong> teach machines to understand images and video. The best learning path in 2026 is: classical OpenCV projects<\/strong> (edge detection, face detection, document scanning) \u2192 CNN classification<\/strong> (cats vs dogs, transfer learning) \u2192 detection and tracking<\/strong> (YOLO, DeepSORT) \u2192 specialized domains<\/strong> (OCR, medical imaging, self-driving) \u2192 generative and multi-modal AI<\/strong> (Stable Diffusion, CLIP, vision-language models). This page lists all 100 ideas with descriptions, tools and datasets.<\/p>\n<\/div>\n Computer vision is the most visible<\/em> branch of AI \u2014 literally. When your phone unlocks with your face, when a car brakes for a pedestrian, when a hospital flags a suspicious X-ray, that’s vision code making a judgment in milliseconds. And here’s the good news: every one of those systems started as someone’s first OpenCV script<\/strong>.<\/p>\n We organized these 100 computer vision project ideas<\/strong> into ten career-aligned, color-coded categories. Start with the green beginner zone \u2014 no GPU required \u2014 then follow your interests into detection, medical AI, autonomous driving or generative imagery. Each idea tells you what you will build and exactly what it teaches.<\/p>\n Tap any category to jump straight to its project list.<\/p>\n The ‘Hello World’ of computer vision – train a simple convolutional neural network to read handwritten digits with 99% accuracy. You will learn image tensors, convolutions, and the full training loop in TensorFlow or PyTorch.<\/p>\n Build a tool that identifies the dominant colors in any photo using K-Means clustering on pixel values. A gentle introduction to treating images as data with OpenCV and NumPy.<\/p>\n Apply Canny, Sobel, and Laplacian edge detectors to your own photos and compare results side by side. Understand gradients and filters – the mathematical heart of classical vision.<\/p>\n Convert photos into pencil sketches and cartoon-style art using Gaussian blurring, bilateral filtering, and edge masks. Instant visual gratification while learning image transformations.<\/p>\n Detect faces in photos and webcam feeds with OpenCV’s classic Haar cascade classifiers. The perfect first step before modern deep learning detectors.<\/p>\n Build a scanner that reads QR codes and barcodes from images or live camera input using OpenCV and pyzbar. A practical project you will actually use.<\/p>\n Stitch overlapping photos into a seamless panorama using feature matching with SIFT\/ORB and homography transforms. Learn how your phone’s panorama mode really works.<\/p>\n Turn a webcam into a motion-triggered security camera using frame differencing and contour detection. Save snapshots whenever movement is detected.<\/p>\n Implement chroma keying that swaps a green background for any image using HSV color masking. The foundation of every video call background filter.<\/p>\n Detect a document’s edges in a photo, correct the perspective, and output a clean scanned-look image. A mini CamScanner built with contour detection and warp transforms.<\/p>\n The classic binary classification challenge – train a CNN to distinguish cats from dogs on 25,000 images. Learn data augmentation, dropout, and how to fight overfitting.<\/p>\n Classify tiny 32×32 images into ten everyday categories like planes, ships, and trucks. A standard benchmark that teaches you to squeeze accuracy from limited resolution.<\/p>\n Fine-tune ResNet, EfficientNet, or MobileNet on your own small dataset and watch accuracy jump with minimal training. The single most practical skill in applied vision.<\/p>\n Identify 101 food dishes from photographs – from samosas to sushi. A deliciously challenging dataset with real-world lighting and plating variety.<\/p>\n Classify 102 flower species from the Oxford Flowers dataset using fine-tuned CNNs. Beautiful data and a genuine fine-grained classification challenge.<\/p>\n Sort waste images into glass, plastic, metal, paper, and organic categories. An environmental AI project with obvious smart-city applications.<\/p>\n Classify clothing images into categories and attributes using the Fashion-MNIST and DeepFashion datasets. The technology behind visual search in e-commerce.<\/p>\n Recognize rupee note denominations from photos – a genuinely useful accessibility tool for visually impaired users when paired with text-to-speech.<\/p>\n Classify images of cricket shots, football kicks, or yoga poses into action categories. Combine pose estimation features with CNN classifiers for higher accuracy.<\/p>\n Train a Vision Transformer and a CNN on the same dataset and benchmark accuracy, speed, and data hunger. Understand why transformers are taking over vision.<\/p>\n Run YOLOv8\/YOLOv9 to detect and label people, cars, and animals in live video at 30+ FPS. Learn bounding boxes, confidence thresholds, and non-max suppression.<\/p>\n Label your own images with Roboflow or LabelImg and train YOLO to detect anything – your pet, a product, a logo. The complete custom detection pipeline.<\/p>\n Count cars, buses, and trucks crossing a virtual line in traffic videos using detection plus centroid tracking. Real transportation analytics in action.<\/p>\n Track multiple people across video frames with persistent IDs by combining YOLO detection and DeepSORT appearance embeddings. The backbone of retail and security analytics.<\/p>\n Detect whether two-wheeler riders are wearing helmets in traffic footage. A high-impact project aligned with road safety enforcement in India.<\/p>\n Measure distances between detected people in CCTV-style video and flag violations using camera calibration and bird’s-eye-view transforms.<\/p>\n Automatically identify animal species in camera-trap images to support conservation research. Handle night-vision images, motion blur, and severe class imbalance.<\/p>\n Detect tiny objects – people, vehicles, boats – in aerial drone imagery using the VisDrone dataset. Learn why small-object detection needs special techniques.<\/p>\n Track 21 hand landmarks in real time with MediaPipe and control your computer’s volume or slides with gestures. Instant futuristic demo material.<\/p>\n Track a cricket or tennis ball across video frames, plot its trajectory, and estimate speed. The DIY version of broadcast Hawk-Eye graphics.<\/p>\n Build an attendance logger that recognizes registered faces from a webcam and records entries with timestamps using the face_recognition library and OpenCV.<\/p>\n Classify faces into emotions – happy, sad, angry, surprised – using CNNs trained on the FER-2013 dataset. Discuss honestly where emotion AI works and where it fails.<\/p>\n Predict approximate age range and gender from face images using pretrained deep models. A study in model bias and responsible reporting of uncertain predictions.<\/p>\n Monitor eye aspect ratio and head pose through a dashcam-style feed and sound an alarm when a driver’s eyes stay closed too long. Vision AI that can save lives.<\/p>\n Detect whether people are wearing masks using transfer learning with MobileNet. A pandemic-era classic that still teaches real-time classification deployment.<\/p>\n Apply lipstick, sunglasses, or fun filters that track facial landmarks in real time – the Snapchat effect built with MediaPipe Face Mesh’s 468 landmarks.<\/p>\n Automatically detect and blur all faces in photos and videos – the privacy tool every news organization and dashcam company needs.<\/p>\n Capture a photo automatically when everyone in frame is smiling, using facial landmark geometry. A delightful beginner-friendly face project.<\/p>\n Estimate where a person is looking by computing head yaw, pitch, and roll from facial landmarks. Used in driver monitoring and attention analytics.<\/p>\n Train a classifier to spot AI-generated face images using artifacts in frequency space and facial inconsistencies. Join the defense side of the deepfake arms race.<\/p>\n Extract merchant, date, and total from receipt photos with Tesseract or EasyOCR, then log expenses to a spreadsheet automatically. Practical OCR with instant personal value.<\/p>\n Detect vehicle number plates and read their text by chaining object detection with OCR. Mirrors real automatic number plate recognition systems used in parking and tolling.<\/p>\n Go beyond digits – recognize full handwritten words and sentences using CRNN or TrOCR transformer models on the IAM handwriting dataset.<\/p>\n Pull structured fields – invoice number, GST, line items, totals – from PDF invoices into JSON using layout-aware models like LayoutLM. High-value business automation.<\/p>\n Recognize a handwritten or printed equation from a photo and solve it with SymPy. The Photomath concept, built end to end.<\/p>\n Detect and read text from street signs in multiple Indian languages and scripts, then translate it. Combines scene-text detection (EAST\/CRAFT) with OCR and translation APIs.<\/p>\n Clean noisy scans of old books with binarization and denoising, then OCR them into searchable text. Cultural preservation meets image processing.<\/p>\n Detect tables in scanned reports and reconstruct them as CSV files, preserving rows and columns. One of the hardest and most requested document AI tasks.<\/p>\n Extract names, phone numbers, and emails from business card photos straight into a contacts file using OCR plus regex and NER post-processing.<\/p>\n Automatically grade multiple-choice OMR sheets from photos using circle detection and thresholding. Every coaching institute wants exactly this tool.<\/p>\n Train a CNN to detect pneumonia in chest radiographs and visualize its reasoning with Grad-CAM heatmaps. The canonical introduction to responsible medical imaging AI.<\/p>\n Classify dermatoscopic images into lesion types using the HAM10000 dataset. Learn about class imbalance, calibration, and why dermatology AI needs careful validation.<\/p>\n Segment tumor regions in MRI scans with a U-Net architecture. Pixel-level prediction – one of the most respected skills in medical vision.<\/p>\n Count and classify red cells, white cells, and platelets in microscope images. Automating one of pathology’s most repetitive tasks.<\/p>\n Grade retinal fundus images by disease severity using the APTOS dataset. A Kaggle-famous challenge with massive screening impact in India.<\/p>\n Detect malaria-infected cells in thin blood smear images with a lightweight CNN that could run on a low-cost field microscope.<\/p>\n Detect and localize fractures in musculoskeletal X-rays using the MURA dataset. Learn how radiologist-AI collaboration is actually evaluated.<\/p>\n Classify and segment lung abnormalities in CT slices – and study the published pitfalls of pandemic-era models as a masterclass in dataset bias.<\/p>\n Segment individual cells in crowded microscopy images using Cellpose or StarDist. A workhorse task across all of biological research.<\/p>\n A science-fair friendly project classifying microscope slide images of plant and animal cells, with clear visual explanations of what features the model uses.<\/p>\n Classify crop diseases from leaf photos using the PlantVillage dataset – hugely relevant for Indian agritech, deployable as a farmer-facing mobile app.<\/p>\n Detect weeds among crop rows in field images to enable targeted spraying. Precision agriculture that cuts herbicide use dramatically.<\/p>\n Grade tomatoes, bananas, or mangoes by ripeness from color and texture features. Straightforward, visual, and directly useful in supply chains.<\/p>\n Count plants and estimate yield from aerial farm images using detection and density-map regression. Agritech startups are built on exactly this capability.<\/p>\n Classify satellite tiles into forest, water, urban, and farmland using EuroSAT data. Your gateway into remote sensing and geospatial deep learning.<\/p>\n Compare satellite images across years to automatically flag deforestation zones. Environmental monitoring with before\/after change-detection networks.<\/p>\n Count and monitor livestock in drone or CCTV footage of farms and pastures. Animal detection with occlusion challenges that sharpen real skills.<\/p>\n Identify fish species from photos for fisheries monitoring and biodiversity studies – useful from aquariums to research vessels.<\/p>\n Detect smoke plumes and fire in camera and satellite imagery for early-warning systems. Latency and false-alarm trade-offs make this a serious engineering exercise.<\/p>\n Classify soil types from close-up photographs using texture analysis and CNNs, helping farmers pick suitable crops without lab testing.<\/p>\n Detect lane lines in dashcam video using Hough transforms, then upgrade to deep segmentation models. The classic first step into autonomous driving.<\/p>\n Classify 43 categories of road signs from the German GTSRB benchmark – a core perception module in every driver-assistance system.<\/p>\n Detect traffic lights and classify their state (red\/yellow\/green) in street scenes. Small objects, high stakes, real-world messiness.<\/p>\n Detect pedestrians and estimate whether they are about to cross using pose cues. The hardest, most safety-critical perception problem on the road.<\/p>\n Predict a depth map from a single camera image using models like MiDaS. Understand how 2D pixels become 3D understanding without LiDAR.<\/p>\n Detect potholes in dashcam footage and map them with GPS – a civic-tech project with obvious value for Indian municipal authorities.<\/p>\n Label every pixel of driving scenes as road, vehicle, person, or building using DeepLab or SegFormer on the Cityscapes dataset.<\/p>\n Estimate a vehicle’s trajectory purely from camera frames using feature tracking and pose estimation. The vision half of SLAM.<\/p>\n Build a Raspberry Pi robot that follows a track using camera input instead of IR sensors. Embedded vision and control on a student budget.<\/p>\n Detect free and occupied parking slots from an overhead camera feed – a smart-city project deployable with one cheap camera per lot.<\/p>\n Bring old family photos to life with pretrained colorization networks – visually stunning results that make portfolios memorable.<\/p>\n Repaint your photos in the style of famous art movements by optimizing content and style losses. The project that made deep learning visually famous.<\/p>\n Train a DCGAN or StyleGAN to generate realistic faces and explore latent-space arithmetic. Study mode collapse and training instability firsthand.<\/p>\n Upscale low-resolution images 4x with ESRGAN or SwinIR, restoring crisp detail. The ‘enhance’ button from crime dramas, actually built.<\/p>\n Fine-tune a diffusion model on your own art style or product photos using LoRA adapters. The hottest generative-vision skill of 2026.<\/p>\n Erase unwanted objects or people from photos and let a diffusion model fill the gap convincingly. The magic-eraser feature, demystified.<\/p>\n Systematically study how prompt wording, negative prompts, and guidance scales change diffusion outputs, and document your findings like a scientist.<\/p>\n Translate sketches into photorealistic images with Pix2Pix or ControlNet. Conditional generation that designers genuinely use.<\/p>\n Combine scratch removal, denoising, face enhancement, and colorization into one restoration pipeline for damaged vintage photographs.<\/p>\n Train a classifier to distinguish real photos from diffusion-generated images – the counterpart to generative AI that newsrooms and platforms urgently need.<\/p>\n Count squats and push-ups and correct posture in real time using MediaPipe or MoveNet pose landmarks. A complete AI fitness product in one project.<\/p>\n Build a system that answers natural-language questions about images using vision-language models like BLIP or LLaVA. Where vision meets LLMs.<\/p>\n Search your photo library with text queries like ‘sunset over water’ using CLIP embeddings and a vector database. Multi-modal retrieval done right.<\/p>\n Generate natural-language captions for photos by pairing a vision encoder with a language decoder – a showcase bridge between two AI worlds.<\/p>\n Turn a set of phone photos into an explorable 3D scene using Neural Radiance Fields or 3D Gaussian Splatting – the frontier of 3D vision.<\/p>\n Deploy quantized detection models on a Raspberry Pi or Jetson Nano and measure FPS, power, and accuracy trade-offs. Real-world deployment engineering.<\/p>\n Classify human actions – cooking, dancing, cricket batting – in video clips using 3D CNNs or video transformers on the Kinetics dataset.<\/p>\n\ud83d\udcda What’s Inside: All 10 Categories at a Glance<\/h2>\n
\n\n
\n \nCategory<\/th>\n Projects<\/th>\n Level<\/th>\n Key Tools<\/th>\n<\/tr>\n<\/thead>\n \n \ud83c\udf31 Beginner Computer Vision Projects<\/a><\/td>\n 1\u201310<\/td>\n Beginner<\/td>\n OpenCV, NumPy<\/td>\n<\/tr>\n \n \ud83d\uddbc\ufe0f Image Classification Projects<\/a><\/td>\n 11\u201320<\/td>\n Beginner+<\/td>\n CNNs, Transfer Learning, ViT<\/td>\n<\/tr>\n \n \ud83c\udfaf Object Detection & Tracking Projects<\/a><\/td>\n 21\u201330<\/td>\n Intermediate<\/td>\n YOLO, DeepSORT, MediaPipe<\/td>\n<\/tr>\n \n \ud83d\ude42 Face Analysis & Recognition Projects<\/a><\/td>\n 31\u201340<\/td>\n Intermediate<\/td>\n face_recognition, MediaPipe<\/td>\n<\/tr>\n \n \ud83d\udcc4 OCR & Document Analysis Projects<\/a><\/td>\n 41\u201350<\/td>\n Intermediate<\/td>\n Tesseract, EasyOCR, LayoutLM<\/td>\n<\/tr>\n \n \ud83e\ude7a Medical & Scientific Imaging Projects<\/a><\/td>\n 51\u201360<\/td>\n Advanced<\/td>\n U-Net, Grad-CAM, PyTorch<\/td>\n<\/tr>\n \n \ud83c\udf3e Agriculture & Environment Projects<\/a><\/td>\n 61\u201370<\/td>\n Intermediate<\/td>\n CNNs, Drone & Satellite Data<\/td>\n<\/tr>\n \n \ud83d\ude97 Autonomous Vehicles & Robotics Projects<\/a><\/td>\n 71\u201380<\/td>\n Advanced<\/td>\n Segmentation, Depth, SLAM<\/td>\n<\/tr>\n \n \ud83c\udfa8 Generative AI & Image Synthesis Projects<\/a><\/td>\n 81\u201390<\/td>\n Advanced<\/td>\n GANs, Stable Diffusion, LoRA<\/td>\n<\/tr>\n \n \ud83d\ude80 Advanced & Real-Time Vision Systems<\/a><\/td>\n 91\u2013100<\/td>\n Expert<\/td>\n CLIP, NeRF, ONNX, MLOps<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n \ud83c\udf31 1. Beginner Computer Vision Projects (Projects 1\u201310)<\/span><\/h2>\n
1<\/span>Handwritten Digit Recognition (MNIST)<\/h3>\n
2<\/span>Image Color Detector<\/h3>\n
3<\/span>Edge Detection Playground<\/h3>\n
4<\/span>Pencil Sketch & Cartoon Filter App<\/h3>\n
5<\/span>Face Detection with Haar Cascades<\/h3>\n
6<\/span>QR Code and Barcode Scanner<\/h3>\n
7<\/span>Image Stitching Panorama Maker<\/h3>\n
8<\/span>Motion Detection Security Camera<\/h3>\n
9<\/span>Green Screen Background Replacement<\/h3>\n
10<\/span>Document Photo Scanner<\/h3>\n
\ud83d\uddbc\ufe0f 2. Image Classification Projects (Projects 11\u201320)<\/span><\/h2>\n
11<\/span>Cats vs Dogs Classifier<\/h3>\n
12<\/span>CIFAR-10 Object Classification<\/h3>\n
13<\/span>Transfer Learning with Pretrained Models<\/h3>\n
14<\/span>Food-101 Dish Recognition<\/h3>\n
15<\/span>Flower Species Classifier<\/h3>\n
16<\/span>Garbage Classification for Recycling<\/h3>\n
17<\/span>Fashion Product Categorization<\/h3>\n
18<\/span>Indian Currency Note Recognition<\/h3>\n
19<\/span>Sports Action Classification<\/h3>\n
20<\/span>Vision Transformer (ViT) vs CNN Showdown<\/h3>\n
\ud83c\udfaf 3. Object Detection & Tracking Projects (Projects 21\u201330)<\/span><\/h2>\n
21<\/span>Real-Time Object Detection with YOLO<\/h3>\n
22<\/span>Custom Object Detector for Your Own Dataset<\/h3>\n
23<\/span>Vehicle Counting on Highway Footage<\/h3>\n
24<\/span>Multi-Object Tracking with DeepSORT<\/h3>\n
25<\/span>Helmet Detection for Road Safety<\/h3>\n
26<\/span>Social Distancing Monitor<\/h3>\n
27<\/span>Wildlife Camera Trap Analyzer<\/h3>\n
28<\/span>Drone-View Object Detection<\/h3>\n
29<\/span>Hand Tracking and Gesture Control<\/h3>\n
30<\/span>Ball Tracking for Sports Analytics<\/h3>\n
\ud83d\ude42 4. Face Analysis & Recognition Projects (Projects 31\u201340)<\/span><\/h2>\n
31<\/span>Face Recognition Attendance System<\/h3>\n
32<\/span>Facial Emotion Detection<\/h3>\n
33<\/span>Age and Gender Estimation<\/h3>\n
34<\/span>Drowsiness Detection for Drivers<\/h3>\n
35<\/span>Face Mask Detection<\/h3>\n
36<\/span>Virtual Makeup and Filter App<\/h3>\n
37<\/span>Face Blurring for Privacy Protection<\/h3>\n
38<\/span>Smile-Triggered Selfie Camera<\/h3>\n
39<\/span>Head Pose Estimation<\/h3>\n
40<\/span>Deepfake Detection Basics<\/h3>\n
\ud83d\udcc4 5. OCR & Document Analysis Projects (Projects 41\u201350)<\/span><\/h2>\n
41<\/span>Receipt Scanner and Expense Tracker<\/h3>\n
42<\/span>License Plate Recognition (ANPR)<\/h3>\n
43<\/span>Handwriting Recognition<\/h3>\n
44<\/span>Invoice Data Extraction Pipeline<\/h3>\n
45<\/span>Math Equation Solver from Photos<\/h3>\n
46<\/span>Multi-Language Sign Board Reader<\/h3>\n
47<\/span>Old Document Digitization & Restoration<\/h3>\n
48<\/span>Table Extraction from Documents<\/h3>\n
49<\/span>Business Card Reader App<\/h3>\n
50<\/span>Answer Sheet \/ OMR Evaluator<\/h3>\n
\ud83e\ude7a 6. Medical & Scientific Imaging Projects (Projects 51\u201360)<\/span><\/h2>\n
51<\/span>Pneumonia Detection from Chest X-Rays<\/h3>\n
52<\/span>Skin Lesion Classification<\/h3>\n
53<\/span>Brain Tumor Segmentation from MRI<\/h3>\n
54<\/span>Blood Cell Counting and Classification<\/h3>\n
55<\/span>Diabetic Retinopathy Grading<\/h3>\n
56<\/span>Malaria Parasite Detection in Cell Images<\/h3>\n
57<\/span>Bone Fracture Detection in X-Rays<\/h3>\n
58<\/span>COVID-19 CT Scan Analysis<\/h3>\n
59<\/span>Microscopy Cell Segmentation<\/h3>\n
60<\/span>Plant Cell vs Animal Cell Classifier<\/h3>\n
\ud83c\udf3e 7. Agriculture & Environment Projects (Projects 61\u201370)<\/span><\/h2>\n
61<\/span>Plant Disease Detection from Leaf Images<\/h3>\n
62<\/span>Weed vs Crop Detection for Smart Farming<\/h3>\n
63<\/span>Fruit Ripeness Grading<\/h3>\n
64<\/span>Crop Yield Estimation from Drone Imagery<\/h3>\n
65<\/span>Satellite Image Land Cover Classification<\/h3>\n
66<\/span>Deforestation Change Detection<\/h3>\n
67<\/span>Cattle and Livestock Counting<\/h3>\n
68<\/span>Fish Species Identification<\/h3>\n
69<\/span>Forest Fire and Smoke Detection<\/h3>\n
70<\/span>Soil Type Classification from Images<\/h3>\n
\ud83d\ude97 8. Autonomous Vehicles & Robotics Projects (Projects 71\u201380)<\/span><\/h2>\n
71<\/span>Lane Detection for Self-Driving Cars<\/h3>\n
72<\/span>Traffic Sign Recognition<\/h3>\n
73<\/span>Traffic Light State Detection<\/h3>\n
74<\/span>Pedestrian Detection and Intent Estimation<\/h3>\n
75<\/span>Monocular Depth Estimation<\/h3>\n
76<\/span>Road Pothole Detection<\/h3>\n
77<\/span>Semantic Segmentation of Street Scenes<\/h3>\n
78<\/span>Visual Odometry from Camera Motion<\/h3>\n
79<\/span>Line-Following Robot with a Camera<\/h3>\n
80<\/span>Parking Space Occupancy Detection<\/h3>\n
\ud83c\udfa8 9. Generative AI & Image Synthesis Projects (Projects 81\u201390)<\/span><\/h2>\n
81<\/span>Photo Colorization of Black & White Images<\/h3>\n
82<\/span>Neural Style Transfer Art Generator<\/h3>\n
83<\/span>GAN for Face Generation<\/h3>\n
84<\/span>Image Super-Resolution Enhancer<\/h3>\n
85<\/span>Stable Diffusion Fine-Tuning with LoRA<\/h3>\n
86<\/span>AI Image Inpainting Object Remover<\/h3>\n
87<\/span>Text-to-Image Prompt Engineering Lab<\/h3>\n
88<\/span>Image-to-Image Sketch to Photo Translation<\/h3>\n
89<\/span>Old Photo Restoration Pipeline<\/h3>\n
90<\/span>AI-Generated Image Detector<\/h3>\n
\ud83d\ude80 10. Advanced & Real-Time Vision Systems (Projects 91\u2013100)<\/span><\/h2>\n
91<\/span>Real-Time Pose Estimation Fitness Coach<\/h3>\n
92<\/span>Visual Question Answering System<\/h3>\n
93<\/span>CLIP-Powered Semantic Image Search<\/h3>\n
94<\/span>Image Captioning with Transformers<\/h3>\n
95<\/span>3D Reconstruction with NeRF \/ Gaussian Splatting<\/h3>\n
96<\/span>Edge AI Vision on Raspberry Pi<\/h3>\n
97<\/span>Video Action Recognition<\/h3>\n
98<\/span>Anomaly Detection for Factory Quality Control<\/h3>\n
![\"Computer](\"https:\/\/1000sciencefairprojects.com\/tech\/wp-content\/uploads\/2026\/06\/computer-vision-projects-featured.png\")
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