An Improved YOLOv5s-Based Helmet Recognition Method for Electric Bikes
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Published:2023-07-28
Issue:15
Volume:13
Page:8759
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ISSN:2076-3417
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Container-title:Applied Sciences
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language:en
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Short-container-title:Applied Sciences
Author:
Huang Bingqiang1, Wu Shanbao1, Xiang Xinjian1, Fei Zhengshun1, Tian Shaohua2, Hu Haibin1, Weng Yunlong1
Affiliation:
1. School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China 2. Key Laboratory of Intelligent Robot for Operation and Maintenance of Zhejiang Province, Hangzhou Shenhao Technology, Hangzhou 310023, China
Abstract
This paper proposes an improved model based on YOLOv5s, specifically designed to overcome the challenges faced by current target detection algorithms in the field of electric bike helmet detection. In order to enhance the model’s ability to detect small targets and densely populated scenes, a specialized layer dedicated to small target detection and a novel loss function called Normalized Wasserstein Distance (NWD) are introduced. In order to solve the problem of increasing model parameters and complexity due to the inclusion of a small target detection layer, a Cross-Stage Partial Channel Mixing (CSPCM) on top of Convmix is designed. The collaborative fusion of CSPCM and the Deep Feature Consistency (DFC) attention mechanism makes it more suitable for hardware devices. In addition, the conventional Nearest Upsample technology is replaced with the advanced CARAFE Upsample module, further improving the accuracy of the model. Through rigorous experiments on carefully constructed datasets, the results show significant improvements in various evaluation indicators such as precision, recall, mAP.5, and mAP.95. Compared with the unmodified YOLOv5s algorithm, the proposed enhanced model achieves significant improvements of 1.1%, 8.4%, 5.2%, and 8.6% on these indicators, respectively, and these enhancements are accompanied by a reduction of 778,924 parameters. The experimental results on our constructed dataset demonstrate the superiority of the improved model and elucidate its potential applications. Furthermore, promising improvements for future research are suggested. This study introduces an efficient approach for improving the detection of electric bike helmets and verifies the effectiveness and practicality of the model through experiments. Importantly, the proposed scheme has implications for other target detection algorithms, especially in the field of small target detection.
Funder
Funded by Open Foundation of the Key Laboratory of Intelligent Robot for Operation and Maintenance of Zhejiang Province Zhejiang Provincial Natural Science Foundation
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference39 articles.
1. Zhang, Q., Zhang, H., and Lu, X. (2022). Adaptive Feature Fusion for Small Object Detection. Appl. Sci., 12. 2. Cui, M., Gong, G., Chen, G., Wang, H., Jin, M., Mao, W., and Lu, H. (2023). LC-YOLO: A Lightweight Model with Efficient Utilization of Limited Detail Features for Small Object Detection. Appl. Sci., 13. 3. Mirri, S., Delnevo, G., and Roccetti, M. (2020). Is a COVID-19 second wave possible in Emilia-Romagna (Italy)? Forecasting a future outbreak with particulate pollution and machine learning. Computation, 8. 4. Concealed object detection;Fan;IEEE Trans. Pattern Anal. Mach. Intell.,2021 5. Histograms of oriented gradients for human detection;Dalal;Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05),2005
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