基于CE-YOLOv5s的安全帽检测算法

Helmet Detection Algorithm Based on CE-YOLOv5s

在环境复杂的施工现场,存在较多危险因素,保护工人的生命安全成为焦点。由于施工现场杂乱的环境和固定的信息采集点,使得安全帽佩戴检测存在漏检和错检问题。因此本文提出一种基于CE-YOLOv5s的安全帽检测算法。该算法将SE注意力机制与C3模块融合,将原网络中C3模块替换,给关键特征赋予更高的权重,抑制一般特征。将一种基于双向特征金字塔网络(BiFPN)的对象检测神经网络引入,同时进行向上和向下的特征融合,为每一个通道添加额外权重,更好地保留低分辨率图像下的细节信息;引入SIoU损失函数,提高边界框定位准确度,加快收敛速度。实验结果表明,改进后的网络模型在精确率、召回率、mAP@0.5和mAP@0.5:0.95上有明显提升,有效提高了安全帽的检测精度,并改善了对杂乱背景下的小目标和被遮挡目标的检测准确率。将本文算法应用于施工场地可以及时检测工人是否做好保护措施,更好地保护工人的生命安全。

In the complex environment of construction sites,there are many dangerous factors,so the protection of the safety of workers has become a focus.Due to the chaotic environment and fixed information collection points at construction sites,there are problems of missed and false detection in safety helmet-wearing detection.Therefore,this paper proposes a safety helmet de⁃tection algorithm based on CE-YOLOv5s.The algorithm combines the SE attention mechanism with the C3 module,replaces the C3 module in the original network,assigns a higher weight to key features,and suppresses general features.Meanwhile,an ob⁃ject detection neural network based on Bi-directional Feature Pyramid Network(BiFPN)is introduced,which performs both up⁃ward and downward feature fusion,adds additional weights to each channel,and better preserves detailed information under lowresolution images.The SIoU loss function is introduced to improve the accuracy of boundary box positioning and accelerate con⁃vergence speed.Experimental results show that the improved network model has significantly improved in precision,recall,mAP@0.5,and mAP@0.5:0.95,effectively improving the detection accuracy of safety helmets and improving the detection accu⁃racy of small targets and obscured targets in cluttered backgrounds.When applied to construction sites,it can timely detect whether workers have taken protective measures,and better protect their safety.