Computer vision system that detects and extracts text from vehicle license plates using OpenCV edge detection and Tesseract OCR. Processes vehicle images to identify plate regions, highlight contours, and read alphanumeric characters automatically.
Deep learning model for automated medical X-ray analysis. Detects and classifies bone fractures using CNN architecture trained on medical imaging datasets to assist radiologists.
Real-time object detection system for road safety compliance monitoring. Detects whether vehicle occupants are wearing seat belts using YOLO architecture on live camera feeds.
8-class deep learning classifier for gastrointestinal diseases from endoscopy images. Built with EfficientNetV2S transfer learning on 8,040 real endoscopy images, achieving 95% test accuracy. Final Year Project at University of Haripur.
End-to-end ML pipeline for HR data, fuzzy name deduplication, feature engineering, scikit-learn classification, exportable performance reports via FastAPI.
Computer vision system that detects smoke and its origin point in images and video using YOLO object detection. Trained across 6 experiments on a custom Roboflow dataset, achieving mAP50 of 0.679 with YOLOv11n, supporting early fire warning and surveillance automation.