J Shanghai Jiaotong Univ Sci ›› 2022, Vol. 27 ›› Issue (6): 833-843.doi: 10.1007/s12204-021-2401-6

• Aeronautics and Astronautics • Previous Articles     Next Articles

Semantic Segmentation-Based Road Marking Detection Using Around View Monitoring System

XU Hanqing1 (徐汉卿), YANG Ming1∗ (杨 明), DENG Liuyuan1 (邓琉元), LI Hao1 (李 颢), WANG Chunxiang1,2(王春香), HAN Weibin3 (韩伟斌), YU Yuelong3 (于跃龙)   

  1. (1. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; 2. SJTU-ParisTech Elite Institute of Technology, Shanghai Jiao Tong University, Shanghai 200240, China; 3. SAIC Motor Corporation Limited, Shanghai 200041, China)
  • Received:2020-06-08 Online:2022-11-28 Published:2022-10-13

Abstract: Road marking detection is an important branch in autonomous driving, understanding the road information. In 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 information. Nevertheless, the previous methods mostly study the training process of the segmentation network, while omitting the time cost of manually annotating pixel-level data. Besides, 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 obtained. In 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 analysis. The proposed detection module is composed of three parts: vote-based segmentation fusion filtering, graph-based road marking clustering, and road-marking fitting. Experiments under various scenarios show that the semantic segmentation-based detection method can achieve accurate, robust, and real-time detection performance.

Key words: autonomous driving, semantic segmentation, road marking detection

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