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| Improve the Helmet Wearing Detection Algorithm for Non-motorized Vehicles Based on YOLOv9 |
| LIU Qi1 ，ZHANG Shuo1 ，CHANG Guangliang2，WANG Ying1
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1.School of Information and Control Engineering，Jilin University of Chemical Technology，Jilin City 132022，China；2. Kesshida (Changchun) Automobile Electrical Appliance Co., LTD., Changchun
130031, China
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Abstract This study proposes an improved YOLOv9-based helmet wearing detection algorithm for non-motor vehicle riders, aiming to solve the problems of low accuracy and high missed detection rate under high-density and complex riding scenarios.Firstly, the Slide Loss loss function was incorporated into YOLOv9 to enhance the model's detection performance on small targets and occluded objects. Secondly, Depthwise Separable Convolution (DSConv) modules were introduced into the backbone network, reducing the model's parameter count. Lastly, a DASI small target detection module was added to the model's Head network, improving the detection capability for small targets. Compared to the YOLOv9 algorithm, the improved algorithm achieved increases of 2.3% in Precision, 1.9% in Recall, and 2.8% in mean Average Precision, while the parameter count was reduced by 9.1% compared to the original model, enabling high-precision detection of non-motor vehicle helmet wearing.
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Published: 11 July 2026
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| [1] |
WANG Ying, LIANG Qiuyang, CHANG Guangliang, LIU Qi. Seed Object Detection Method based on Improved YOLOv8[J]. Journal of Jilin Institute of Chemical Technology, 2025, 42(7): 17-23. |
| [2] |
LIU Qi , LI Kuidong , CHANG Guangliang, WANG Ying. Fruit Target Detection Method based on Improved YOLOv7[J]. Journal of Jilin Institute of Chemical Technology, 2024, 41(7): 12-17. |
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