DFALaneNet: A Dynamic Real-Time Lane Detection Network Based on Adaptive Scheduling

作     者：He, Wen Wei, Yu Ren, Fan Liu, Mingjie Chen, Yong Hao, Jinlong Li, Yan 

作者机构：Univ Elect Sci & Technol China Sch Automat Engn Chengdu Peoples R China Changan Automot Software Technol Co Ltd Chongqing Peoples R China Chongqing Univ Posts & Telecommun Sch Automat Chongqing Peoples R China Inha Univ Dept Elect & Comp Engn Incheon South Korea 

出 版 物：《HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES》 (Hum.-centric Comput. Inf. Sci.)

年 卷 期：2025年第15卷

核心收录：

基　　金：National Key RD Program [2022YFE0101000] Science and Technology Research Program of the Chongqing Municipal Education Commission [KJQN202200630] INHA University 

主　　题：Lane Detection Image Segmentation Adaptive Scheduling Network 

摘      要：Lane detection is an important aspect of autonomous driving. For real-world applications, both accuracy and processing time are very important for this technology. Thus, this study proposed a dynamic real-time deep learning-based lane detection method based on the adaptive scheduling of input frames. An adaptive scheduling network was proposed to separate the input into key and adjacent frames based on the expected confidence. Consequently, an improved LaneNet encoder-decoder model was adopted as the primary image segmentation network, which was used to perform lane detection on a keyframe to ensure high accuracy and robustness. A neural optical flow network was used to perform lane predictions on adjacent frames to improve the speed of the detection network. We evaluated our proposed method on TuSimple, UCLane, and our campus datasets to demonstrate its effectiveness. The experimental results indicated that the proposed method performed well on both challenging public and self-made campus datasets, with substantial improvements compared with certain well-known methods.