Author:
Qiu Chengqun,Tang Hao,Yang Yuchen,Wan Xinshan,Xu Xixi,Lin Shengqiang,Lin Ziheng,Meng Mingyu,Zha Changli
Abstract
AbstractThe resolution of traffic congestion and personal safety issues holds paramount importance for human’s life. The ability of an autonomous driving system to navigate complex road conditions is crucial. Deep learning has greatly facilitated machine vision perception in autonomous driving. Aiming at the problem of small target detection in traditional YOLOv5s, this paper proposes an optimized target detection algorithm. The C3 module on the algorithm’s backbone is upgraded to the CBAMC3 module, introducing a novel GELU activation function and EfficiCIoU loss function, which accelerate convergence on position loss lbox, confidence loss lobj, and classification loss lcls, enhance image learning capabilities and address the issue of inaccurate detection of small targets by improving the algorithm. Testing with a vehicle-mounted camera on a predefined route effectively identifies road vehicles and analyzes depth position information. The avoidance model, combined with Pure Pursuit and MPC control algorithms, exhibits more stable variations in vehicle speed, front-wheel steering angle, lateral acceleration, etc., compared to the non-optimized version. The robustness of the driving system's visual avoidance functionality is enhanced, further ameliorating congestion issues and ensuring personal safety.
Funder
National Key R&D Program of China
Jiangsu Natural Science Foundation of China
Open project of Jiangsu Intelligent Optoelectronic Devices and Measurement and Control Engineering Research Center
Teaching Research Foundation of Yancheng Teachers University
Postgraduate Research and Practice Innovation Project of Yancheng Institute of Technology
Publisher
Springer Science and Business Media LLC
Cited by
1 articles.
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