Lane Detection Based on Image Processing and Machine Learning

Abstract

Lane detection and object detection are crucial tasks in the field of autonomous driving. In this project, we propose a system that combines both lane and object detection to improve the performance and safety of autonomous vehicles. The project uses Haar-like features and Cascade classifiers to detect objects and lane markings from the video frames. The lane detection algorithm detects lane markings on the road by analyzing the color and edge features of the image. The object detection algorithm uses a pre-trained Cascade classifier to detect objects in the video frames. The system is implemented using Python libraries and Yolo. The results show that the system is able to accurately detect lane markings and objects in the video streams with high accuracy and low latency.