Integration of OpenCV and YoloV5 This project focuses on detecting road lane lines and objects using advanced computer vision techniques. It is designed to enhance driving safety by highlighting the lane in which the vehicle is traveling while simultaneously detecting objects on the road.
- Title: Road Lane Line Detection and Object Detection
- Domain: Autonomous Driving, Advanced Driver Assistance Systems (ADAS)
- Technologies Used: YOLOv5, OpenCV, Python
- Lane Line Detection: Highlights the lane in which the car is traveling.
- Object Detection: Identifies vehicles, pedestrians, and other obstacles on the road using YOLOv5.
- Video Processing Pipeline: Processes input video streams frame by frame for real-time results.
- Adaptability: Works in various environmental conditions (daylight, night, rain, etc.).
- Python 3.7+
- Install the required Python packages:
- YOLOV5 and openCV
- Input video - road-video-russia.mp4
- Output video - lane_and_object_tracker.mp4