OpenCV

Autonomous Vehicle Object Detection System: Developed a comprehensive object detection system for autonomous vehicles using OpenCV that processes real-time camera feeds to identify vehicles, pedestrians, traffic signs, and road obstacles. The challenge involved creating algorithms that could reliably detect objects in varying lighting conditions, different weather scenarios, and complex traffic environments while maintaining the processing speed necessary for real-time autonomous driving decisions. Successfully implemented multi-scale object detection with confidence scoring that enables the autonomous vehicle to make appropriate navigation decisions based on detected objects and their relative positions.

Lane Detection and Tracking Algorithm: Architected a sophisticated lane detection system using OpenCV that identifies road lane markings, tracks lane boundaries, and provides continuous feedback for autonomous vehicle steering control. The project required implementing edge detection algorithms, Hough line transforms, and perspective transformation techniques to accurately identify lane geometry even in challenging conditions like curved roads, faded markings, and variable lighting. Successfully created a robust lane tracking system that maintains accuracy across diverse driving conditions and integrates seamlessly with vehicle control systems.

Multi-Sensor Computer Vision Integration: Integrated OpenCV image processing capabilities with TensorFlow neural networks to create a comprehensive computer vision system for autonomous vehicles that combines traditional computer vision techniques with deep learning approaches. The challenge involved coordinating OpenCV's real-time processing capabilities with TensorFlow's advanced pattern recognition to create a hybrid system that leverages the strengths of both approaches. Successfully developed a system that uses OpenCV for rapid initial processing and feature extraction while applying TensorFlow models for complex pattern recognition and decision-making.

Real-Time Environmental Analysis Pipeline: Created a high-performance image processing pipeline using OpenCV that analyzes multiple camera feeds simultaneously to provide comprehensive environmental awareness for autonomous vehicle navigation. The system required optimizing algorithm performance to handle multiple high-resolution video streams while maintaining the low latency necessary for safe autonomous operation. Successfully implemented efficient memory management, parallel processing techniques, and algorithm optimization that enables real-time analysis of complex driving environments.