The research on edge detection algorithm of lane

• Main Authors: Zhong-xun Wang, Wenqi Wang
• Format: Article
• Language: English
• Published: SpringerOpen 2018-10-01
• Series: EURASIP Journal on Image and Video Processing
• Subjects: Lane line detection and identification; Kirsch algorithm; MATLAB
• Online Access: http://link.springer.com/article/10.1186/s13640-018-0326-2

Abstract In developed countries, the automation and intelligent development of vehicles has reached a relatively high level and has gradually developed in China. In recent years, algorithms for lane detection have emerged in an endless stream, but the advantages and disadvantages of comprehensive comparison of various algorithms have resulted in the following problems: First, the robustness of lane line detection is poor, mainly due to the surrounding environment of the road. The impact is greater, in traffic-intensive city streets, affected by natural factors such as trees or building shadows around the driveway; second, the real-time nature of the lane line detection is poor, affected by other marking lines on the driveway, or damaged in the lane. When the pollution is serious, the image of the lane line collected by the system is incomplete and of poor quality, which increases the difficulty of analyzing and processing data of the detection system. This article aims at the problems existing in the current lane line detection and determines the research focus of the article: (1) Improve the accuracy and real-time performance of the detection algorithm. Most of the factors affecting the detection of the lane line are generated before the image is acquired. This requires the strict pre-processing of the collected lane line image to remove a large amount of interference. After the information, not only can the detection result be more accurate but also the complexity of the algorithm be simplified, making the detection result accurate and effective. (2) Use the FPGA for verification. This paper simulates the detection algorithm from two aspects of MATLAB and FPGA, learns the working mode of the related chip and different interface protocols, optimizes the logic design through the hardware design language, and facilitates the hardware implementation of the algorithm. This method can effectively improve the image processing speed, save the logic resources, and better realize the lane recognition function. (3) Improve the existing lane line detection algorithm. There are many algorithms for lane detection, but each algorithm has its own advantages and disadvantages. Part of this article summarizes the advantages and disadvantages of these detection algorithms, analyzes their feasibility in actual detection, and improves the algorithm based on this.