Research on Road Scene Understanding of Autonomous Vehicles Based on Multi-Task Learning.
Sensors (Basel)
; 23(13)2023 Jul 07.
Article
em En
| MEDLINE
| ID: mdl-37448087
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.
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Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Veículos Autônomos
/
Aprendizagem
Idioma:
En
Revista:
Sensors (Basel)
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
China