基于改进YOLO的不规范佩戴安全帽检测

Detection of Nonstandard Wearing of Safety Helmet Based on Improved YOLO

为改善现有变电站巡检人员不规范佩戴安全帽检测时效率、精度低的问题,提出一种基于改进YOLO的轻量化变电站人员不规范行为检测模型。该模型由特征提取网络、ECA-SPP和ECA-PANet网络以及预测网络组成;特征提取网络中使用MobileNetV3;提取4个尺度的特征图并将其输入到SPP和PANet网络中,并基于注意力机制进行优化;以建立的变电站人员不规范佩戴安全帽检测数据集为例,验证所提模型有效性。实验结果表明:所提模型mAP为0.8244,FPS为38.06,明显优于Faster RCNN、YOLOv4、YOLOx等模型,具有较高精度和更快的检测速度,可为变电站人员不规范佩戴安全帽的实时检测提供参考。

In order to solve the problem of low efficiency and accuracy in the detection of non-standard safety helmet worn by the existing substation patrol personnel,a lightweight substation personnel non-standard behavior detection model based on improved YOLO is proposed.The model consists of a feature extraction network,an ECA-SPP network,an ECA-PANet network and a prediction network;MobileNet V3 is used in the feature extraction network;feature maps of four scales are extracted and input into the SPP and PANet networks,and are optimized based on an attention mechanism;The effectiveness of the proposed model is verified by the data set of the detection of non-standard wearing of safety helmets in substations.The experiment results show that the proposed model mAP is a 0.8244 and FPS is a 38.06,which is obviously better than other models such as Faster RCNN,YOLOv4 and YOLOx,and has higher accuracy and faster detection speed.It can provide a reference for real-time detection of substation personnel wearing non-standard safety helmet.