改进YOLOv7的轧钢车间安全帽佩戴检测算法  

Helmet Wearing Detection Algorithm in Steel Rolling Workshops Based on Improved YOLOv7

作　　者：张欣毅 张运楚[1,2] 王菲 刘一铭 ZHANG Xinyi;ZHANG Yunchu;WANG Fei;LIU Yiming(School of Information and Electrical Engineering,Shandong Jianzhu University,Jinan 250101,China;Shandong Key Laboratory of Intelligent Buildings Technology,Jinan 250101,China)

机构地区：[1]山东建筑大学信息与电气工程学院,济南250101 [2]山东省智能建筑技术重点实验室,济南250101

出　　处：《计算机测量与控制》2024年第7期15-22,共8页Computer Measurement &Control

基　　金：国家自然科学基金(62003191)。

摘　　要：佩戴安全帽能够保护生产工作者头部免受坠物撞击带来的伤害;轧钢车间存在空间跨度大、作业设备繁多、环境杂乱、昼夜光照差别大、炫光、监控目标尺度变化范围大等问题,增加了安全帽佩戴检测难度;针对上述问题,设计了基于改进YOLOv7模型的轧钢车间安全帽佩戴检测方案;算法基于NWD方法改进损失函数以提高目标检测精度,并在SPPCSPC模块上增加了BiFormer模块,使模型对小目标具有更好的检测精度,同时不会增加运算负担,优于其他注意力机制;在自建安全帽数据集上对改进的YOLOv7模型进行训练,实验表明,改进的YOLOv7模型平均精度均值为99.3%,检测速度达82FPS,与其他主流算法、改进算法对比,改进YOLOv7的mAP指标最高,大大超过了其他模型的指标,同时检测速度基本与改进模型前相差不大,并没有因为精度提高而明显降低检测速度,有较好效果。Wearing helmets can protect the head of production workers from injuries caused by falling objects.There are problems such as large span of space,numerous operating devices,cluttered environment,large difference in lighting between day and night,dazzling light,and significant changes of monitoring target in steel rolling workshops,increasing the difficulty of helmet wearing detection.In response to the above problems,a helmet wearing detection scheme based on improved YOLOv7 model is designed in steel rolling workshops.Based on normalized Wasserstein distance(NWD)method,the algorithm improves the loss function to increase the accuracy of target detection,the BiFormer module is added on the SPPCSPC module,which makes the model have better detection accuracy for small targets without increasing the computational cost,it is superior to other attention mechanisms.The improved YOLOv7 model is trained on the self-constructed helmet dataset,the experimental results show that the improved YOLOv7 model has a mean average accuracy of 99.3%,with a detection speed of 82 FPS.comparing with other mainstream algorithms and improved algorithms,the improved YOLOv7 has the highest mAP index,the index of the improved YOLOv7 is much more than that of other models.At the same time,the detection speed is not much different from that before the improvement of the model,it does not significantly reduce the detection speed because of the improvement of accuracy,which has a better effect.

关 键 词：轧钢车间 安全帽检测 YOLOv7 NWD BiFormer 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]