基于改进YOLO 的不规范佩戴安全帽检测

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0.824 4，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。

Detection of Nonstandard Wearing of Safety Helmet Based on Improved YOLO

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.