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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Bibliographic Details
Published in:IEEE access 2024, Vol.12, p.176686-176695
Main Authors: Zeng, Bing, Liao, Zhanpeng, Zhou, Yu, He, Dilin, Zhou, Zhihao, Yi, Kexin, Li, Yuwen, Yang, Xiaopin, Xie, Yunmin
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Attention mechanisms
Complexity
Computational efficiency
Computational modeling
Computer architecture
Feature extraction
Head
Lightweight
MobileNetV3-SC
Modules
MSS-YOLO
Neck
Object recognition
Parameters
Pharmacists
Safety
safety helmet
Safety helmets
SC attention mechanism
simSPPF-N
Target detection
Weight reduction
YOLO
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Summary: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.
ISSN:2169-3536
DOI:10.1109/ACCESS.2024.3506056