A YOLOv7-based target detection method for insulated gloves and insulated rubber shoes was proposed to address the risk of safety accidents caused by workers in coal mine substations not wearing insulated gloves and insulated rubber shoes.Using depthwise separable convolution improved the model's feature extraction ability while reducing network computation.By introducing the SE attention mechanism,the ELAN1 and SPPCSPC modules were reconstructed to enhance the network's feature extraction capability and improve the accuracy of small object detection.The WIoU loss function was employed to enhance the accuracy of network regression.The experimental results indicated that the improved YOLOv7 algorithm increased accuracy by 3.8％and mAP by 12％compared to the original algorithm,allowing for efficient and real-time detection of unsafe behavior related to not wearing insulated gloves and insulated rubber shoes.
关键词
绝缘手套/绝缘胶鞋/YOLOv7/深度可分离卷积/SE注意力机制/WIoU损失函数/不安全行为
Key words
insulated gloves/insulated rubber shoes/YOLOv7/depthwiseseparable convolution/SE attention mechanism/WIoU loss function/unsafe behavior
维普
万方数据