Citation

Guo J, Wang J, Wang H, Xiao B, He Z, Li L. Sensors (Basel) 2023; 23(13).

Copyright

(Copyright © 2023, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s23136238

PMID

37448087

PMCID

PMC10346996

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.

Language: en

Keywords

autonomous vehicles; drivable area detection; lane line detection; multi-task learning; traffic object detection; visual perception