1. Research on Road Scene Understanding of Autonomous Vehicles Based on Multi-Task Learning
- Author
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Jinghua Guo, Jingyao Wang, Huinian Wang, Baoping Xiao, Zhifei He, and Lubin Li
- Subjects
autonomous vehicles ,visual perception ,multi-task learning ,traffic object detection ,drivable area detection ,lane line detection ,Chemical technology ,TP1-1185 - Abstract
Road scene understanding is crucial to the safe driving of autonomous vehicles. Comprehensive road scene understanding requires a visual perception system to deal with a large number of tasks at the same time, which needs a perception model with a small size, fast speed, and high accuracy. As multi-task learning has evident advantages in performance and computational resources, in this paper, a multi-task model YOLO-Object, Drivable Area, and Lane Line Detection (YOLO-ODL) based on hard parameter sharing is proposed to realize joint and efficient detection of traffic objects, drivable areas, and lane lines. In order to balance tasks of YOLO-ODL, a weight balancing strategy is introduced so that the weight parameters of the model can be automatically adjusted during training, and a Mosaic migration optimization scheme is adopted to improve the evaluation indicators of the model. Our YOLO-ODL model performs well on the challenging BDD100K dataset, achieving the state of the art in terms of accuracy and computational efficiency.
- Published
- 2023
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