Unsafe behaviour detection with the improved YOLOv5 model

IF 1.7 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Li Ying, Zhao Lei, Geng Junwei, Hu Jinhui, Ma Lei, Zhao Zilong
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引用次数: 0

Abstract

In industrial environments, workers should wear workwear for safety considerations. For the same reason, smoking is also prohibited. Due to the supervision of monitoring devices, workers have reduced smoking behaviours and started wearing workwear. To meet the requirements for detecting these behaviours in real-time monitoring videos with high speed and accuracy, the authors proposed an improved YOLOv5 model with the Triplet Attention mechanism. This mechanism strengthens the connection between channel and spatial dimensions, focuses the network on important parts, and improves feature extraction. Compared to the original YOLOv5 model, the addition of the mechanism increases the parameters by only 0.04%. The recall rate of the YOLOv5 model is enhanced while its prediction speed is maintained with only a minimal increase in parameters. Experiment results show that, compared to the original model, the improved YOLOv5 has a recall rate of 78.8%, 91%, and 89.3% for detecting smoking behaviour, not wearing helmets, and inappropriate workwear, respectively.

Abstract Image

使用改进的 YOLOv5 模型检测不安全行为
在工业环境中,出于安全考虑,工人应穿工作服。出于同样的原因,也禁止吸烟。在监控设备的监督下,工人们减少了吸烟行为,并开始穿戴工作服。为了满足在实时监控视频中高速、准确地检测这些行为的要求,作者提出了一种具有三重注意机制的改进型 YOLOv5 模型。该机制加强了通道和空间维度之间的联系,使网络聚焦于重要部分,并改进了特征提取。与最初的 YOLOv5 模型相比,加入该机制后参数只增加了 0.04%。YOLOv5 模型的召回率得到了提高,同时其预测速度也得到了保持,而参数的增加却微乎其微。实验结果表明,与原始模型相比,改进后的 YOLOv5 在检测吸烟行为、不戴头盔和不合适工作服方面的召回率分别为 78.8%、91% 和 89.3%。
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来源期刊
IET Cyber-Physical Systems: Theory and Applications
IET Cyber-Physical Systems: Theory and Applications Computer Science-Computer Networks and Communications
CiteScore
5.40
自引率
6.70%
发文量
17
审稿时长
19 weeks
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