[1]HU Dandan,ZHANG Zhongting.Road target detection algorithm for autonomous driving scenarios based on improved YOLOv5s[J].CAAI Transactions on Intelligent Systems,2024,19(3):653-660.[doi:10.11992/tis.202206034]
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Road target detection algorithm for autonomous driving scenarios based on improved YOLOv5s

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 3 Page number: 653-660 Column: 学术论文—智能系统 Public date: 2024-05-05
Title:
Road target detection algorithm for autonomous driving scenarios based on improved YOLOv5s
Author(s):
HU Dandan;  ZHANG Zhongting
Robotics Institute, Civil Aviation University of China, Tianjin 300300, China
Keywords:
YOLOv5s; autonomous driving; target detection algorithm; depthwise separable convolution; receptive field block; adaptive spatial feature fusion; PANet; multiscale feature fusion
CLC:
TP391.4
DOI:
10.11992/tis.202206034
Abstract:
When vehicles, pedestrians, bicycles, and other targets are detected in complex road scenes, the existence of multiscale targets and partial occlusions may easily cause missed and false detections. In this paper, a road target detection algorithm is proposed based on improved YOLOv5s, orienting to autonomous driving scenarios. First, depthwise separable convolution is used to replace partial ordinary convolutions to reduce the number of parameters of the model to improve the detection speed. An improved RFB-s based on receptive field block (RFB) is introduced into the feature fusion network to enhance the effective receptive field area of the feature map, improving the network feature expression capability and the recognizability of the target features by imitating human visual perception. Finally, an adaptive spatial feature fusion method is used to enhance the effect of PANet on multiscale feature fusion. The experimental results reveal that, on the PASCAL VOC dataset, compared with YOLOv5s, the mean value of the average detection precision of the proposed algorithm is improved by 1.71%, reaching 84.01%. Under the premise of meeting the real-time requirement of autonomous driving vehicles, this algorithm has reduced false and missed detections in the target detection to a certain extent, effectively improving the detection performance of the model in complex driving scenarios.
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