Computer Vision Projects by Application
CV projects are impressive because you can see the results. A face detector that draws bounding boxes on a video feed, an object counter that tracks items in real-time, a document scanner that straightens and enhances photos — these are tangible outputs that make great demos.
Face Detection and Recognition
Face projects use OpenCV's Haar cascades for basic detection and deep learning models (FaceNet, ArcFace) for recognition. You'll find attendance systems that recognize faces from a webcam, face verification systems that compare two photos, and emotion detection that classifies facial expressions. Each project handles multiple faces in a frame and works with live camera input.
Object Detection with YOLO
Our YOLO projects use YOLOv5 and YOLOv8 for real-time object detection. Applications include vehicle counting at intersections, safety equipment detection on construction sites, product recognition in retail, and wildlife monitoring. Each project includes the trained weights, inference scripts for images and video, and a web interface for uploading images.
Image Segmentation
Segmentation projects use U-Net and Mask R-CNN architectures. Applications include medical image segmentation (tumor detection, cell counting), satellite image analysis (land use classification), and document layout analysis. These projects output pixel-level masks showing exactly what the model identified.
OCR and Document Processing
OCR projects use Tesseract with preprocessing (deskewing, noise removal, binarization) and deep learning-based OCR (CRNN, EAST text detector). Applications include invoice data extraction, handwriting recognition, and license plate reading. Each project handles real-world image quality — not just clean, well-lit scans.
