An Investigation Into a Lightweight Safety Helmet Detection Approach Utilizing the MSS-YOLO Framework
Motivated by the challenges of low detection accuracy, false positives, false negatives, and the excessive computational demand in safety helmet detection within complex power scenarios, this paper introduces a lightweight safety helmet object detection algorithm based on MSS-YOLO. Initially, the pr...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10767142/ |
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| author | Bing Zeng Zhanpeng Liao Yu Zhou Dilin He Zhihao Zhou Kexin Yi Yuwen Li Xiaopin Yang Yunmin Xie |
| author_facet | Bing Zeng Zhanpeng Liao Yu Zhou Dilin He Zhihao Zhou Kexin Yi Yuwen Li Xiaopin Yang Yunmin Xie |
| author_sort | Bing Zeng |
| collection | DOAJ |
| description | Motivated by the challenges of low detection accuracy, false positives, false negatives, and the excessive computational demand in safety helmet detection within complex power scenarios, this paper introduces a lightweight safety helmet object detection algorithm based on MSS-YOLO. Initially, the proposed lightweight backbone network, MobileNetV3-SC, replaces the original DarkNet53 to reduce complexity. Subsequently, an improved lightweight spatial pyramid pooling module, simSPPF-N(simplified SPPF-NAM), is appended to the backbone network, facilitating a more effective amalgamation of local and global features. Additionally, the SC(Spatial and Channel Attention Mechanism) lightweight attention mechanism module is incorporated into the upsampling layers to enhance the algorithm’s focus on targets. The CIoU loss function supersedes the conventional IoU, thereby boosting detection accuracy. The experimental outcomes reveal that compared to its predecessor, the proposed algorithm achieves a significant reduction in parameters from 61.949M to 13.547M and in computational load from 66.171G to 7.895G, while simultaneously elevating mAP@0.5 from 81.99% to 91.20%. This demonstrates the algorithm’s capability to maintain high detection accuracy while drastically minimizing its parameter size and computational overhead. |
| format | Article |
| id | doaj-art-c28961de807d49dfb4072c6caedcfbc0 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c28961de807d49dfb4072c6caedcfbc02025-01-16T00:01:39ZengIEEEIEEE Access2169-35362024-01-011217668617669510.1109/ACCESS.2024.350605610767142An Investigation Into a Lightweight Safety Helmet Detection Approach Utilizing the MSS-YOLO FrameworkBing Zeng0https://orcid.org/0000-0002-1757-0558Zhanpeng Liao1https://orcid.org/0009-0006-3592-263XYu Zhou2Dilin He3Zhihao Zhou4https://orcid.org/0009-0007-3224-0577Kexin Yi5Yuwen Li6Xiaopin Yang7https://orcid.org/0000-0001-8920-9318Yunmin Xie8Nanchang Institute of Technology, Nanchang, ChinaNanchang Institute of Technology, Nanchang, ChinaNanchang Institute of Technology, Nanchang, ChinaNanchang Institute of Technology, Nanchang, ChinaNanchang Institute of Technology, Nanchang, ChinaNanchang Institute of Technology, Nanchang, ChinaNanchang Institute of Technology, Nanchang, ChinaNanchang Institute of Technology, Nanchang, ChinaNanchang Institute of Technology, Nanchang, ChinaMotivated by the challenges of low detection accuracy, false positives, false negatives, and the excessive computational demand in safety helmet detection within complex power scenarios, this paper introduces a lightweight safety helmet object detection algorithm based on MSS-YOLO. Initially, the proposed lightweight backbone network, MobileNetV3-SC, replaces the original DarkNet53 to reduce complexity. Subsequently, an improved lightweight spatial pyramid pooling module, simSPPF-N(simplified SPPF-NAM), is appended to the backbone network, facilitating a more effective amalgamation of local and global features. Additionally, the SC(Spatial and Channel Attention Mechanism) lightweight attention mechanism module is incorporated into the upsampling layers to enhance the algorithm’s focus on targets. The CIoU loss function supersedes the conventional IoU, thereby boosting detection accuracy. The experimental outcomes reveal that compared to its predecessor, the proposed algorithm achieves a significant reduction in parameters from 61.949M to 13.547M and in computational load from 66.171G to 7.895G, while simultaneously elevating mAP@0.5 from 81.99% to 91.20%. This demonstrates the algorithm’s capability to maintain high detection accuracy while drastically minimizing its parameter size and computational overhead.https://ieeexplore.ieee.org/document/10767142/MSS-YOLOsafety helmetlightweightMobileNetV3-SCsimSPPF-NSC attention mechanism |
| spellingShingle | Bing Zeng Zhanpeng Liao Yu Zhou Dilin He Zhihao Zhou Kexin Yi Yuwen Li Xiaopin Yang Yunmin Xie An Investigation Into a Lightweight Safety Helmet Detection Approach Utilizing the MSS-YOLO Framework IEEE Access MSS-YOLO safety helmet lightweight MobileNetV3-SC simSPPF-N SC attention mechanism |
| title | An Investigation Into a Lightweight Safety Helmet Detection Approach Utilizing the MSS-YOLO Framework |
| title_full | An Investigation Into a Lightweight Safety Helmet Detection Approach Utilizing the MSS-YOLO Framework |
| title_fullStr | An Investigation Into a Lightweight Safety Helmet Detection Approach Utilizing the MSS-YOLO Framework |
| title_full_unstemmed | An Investigation Into a Lightweight Safety Helmet Detection Approach Utilizing the MSS-YOLO Framework |
| title_short | An Investigation Into a Lightweight Safety Helmet Detection Approach Utilizing the MSS-YOLO Framework |
| title_sort | investigation into a lightweight safety helmet detection approach utilizing the mss yolo framework |
| topic | MSS-YOLO safety helmet lightweight MobileNetV3-SC simSPPF-N SC attention mechanism |
| url | https://ieeexplore.ieee.org/document/10767142/ |
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