Semantic Segmentation-Based Road Marking Detection Using Around View Monitoring System
作者机构：School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China SJTU-ParisTech Elite Institute of TechnologyShanghai Jiao Tong UniversityShanghai 200240China SAIC Motor Corporation LimitedShanghai 200041China
出 版 物：《Journal of Shanghai Jiaotong university(Science)》 (上海交通大学学报（英文版）)
年 卷 期：2022年第27卷第6期
页 面：833-843页
核心收录：
学科分类：08[工学] 0825[工学-航空宇航科学与技术]
基 金：the National Natural Science Foundation of China(Nos.U1764264 and 61873165) the Shanghai Automotive Industry Science and Technology Development Foundation(No.1807)
主 题：autonomous driving semantic segmentation road marking detection
摘 要：Road marking detection is an important branch in autonomous driving,understanding the road *** recent years,deep-learning-based semantic segmentation methods for road marking detection have been arising since they can generalize detection result well under complicated environments and hold rich pixel-level semantic ***,the previous methods mostly study the training process of the segmentation network,while omitting the time cost of manually annotating pixel-level ***,the pixel-level semantic segmentation results need to be fitted into more reliable and compact models so that geometrical information of road markings can be explicitly *** order to tackle the above problems,this paper describes a semantic segmentation-based road marking detection method using around view monitoring system.A semiautomatic semantic annotation platform is developed,which exploits an auxiliary segmentation graph to speed up the annotation process while guaranteeing the annotation accuracy.A segmentation-based detection module is also described,which models the semantic segmentation results for the more robust and compact *** proposed detection module is composed of three parts:vote-based segmentation fusion filtering,graph-based road marking clustering,and road-marking *** under various scenarios show that the semantic segmentation-based detection method can achieve accurate,robust,and real-time detection performance.
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