Neural Network Based on Multi-Scale Saliency Fusion for Traffic Signs Detection

Haohao Zou, Huawei Zhan () and Linqing Zhang
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Haohao Zou: School of Electronic and Electrical Engineering, Henan Normal University, Xinxiang 453007, China
Huawei Zhan: School of Electronic and Electrical Engineering, Henan Normal University, Xinxiang 453007, China
Linqing Zhang: School of Electronic and Electrical Engineering, Henan Normal University, Xinxiang 453007, China

Sustainability, 2022, vol. 14, issue 24, 1-12

Abstract: Aiming at recognizing small-scale and complex traffic signs in the driving environment, a traffic sign detection algorithm YOLO-FAM based on YOLOv5 is proposed. Firstly, a new backbone network, ShuffleNet-v2, is used to reduce the algorithm’s parameters, realize lightweight detection, and improve detection speed. Secondly, the Bidirectional Feature Pyramid Network (BiFPN) structure is introduced to capture multi-scale context information, so as to obtain more feature information and improve detection accuracy. Finally, location information is added to the channel attention using the Coordinated Attention (CA) mechanism, thus enhancing the feature expression. The experimental results show that compared with YOLOv5, the mAP value of this method increased by 2.27%. Our approach can be effectively applied to recognizing traffic signs in complex scenes. At road intersections, traffic planners can better plan traffic and avoid traffic jams.

Keywords: multi-scale context; traffic sign; attention; complex scenes; YOLOv5 (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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