Construction Machinery Vehicle Control System Based on Machine Vision

Abstract

With the development of artificial intelligence, intelligent vehicle is very important in its. In this paper, a control system based on machine vision is designed by integrating the technology of image processing, pattern recognition, intelligent control and embedded system. The system realizes the road environment perception, path planning and decision-making, vehicle motion control and other functions. This paper establishes the overall framework of the intelligent driving system, and designs the software and hardware system around the three subsystems. In this paper, a variety of filtering and edge detection algorithms of machine vision image processing technology are compared and improved methods are proposed. The adaptive threshold method is used to segment the image. The improved Prewitt operator and edge tracking method are used for edge detection and boundary extraction. Hough transform is used to detect lanes and connect intermittent line segments. Then, secondary feature extraction is performed on the boundary image. Combined with the system requirements, the related control algorithm is discussed, and the fuzzy PID control algorithm is proposed and simulated and verified. The experimental results show that the system control method based on machine vision can improve the control efficiency and control accuracy.