Real-time detection algorithm of underground personnel behavior based on YOLOv8-ECW

The existing models for detecting the behaviors of personnel in coal mine wells suffer from issues such as low accuracy and significant computational load. Therefore, a real-time detection algorithm for the behaviors of underground personnel based on YOLOv8-ECW is proposed. Based on YOLOv8n, the backbone network is enhanced by presenting the multi-scale convolution module EMSC. It is combined with the C2f convolution to design the C2f_EMSC module, effectively capturing the multi-scale features of the target and reducing the computational volume and parameter quantity of the model. The CGBlock downsampling module is introduced into the network to fuse the global context information. The WIoU loss function is incorporated to enhance the positioning accuracy of the detection box and the convergence speed of the model. Experiments conducted on the self-established dataset for detecting the behaviors of personnel in coal mines reveal the following results: ① Compared with the baseline YOLOv8n model, the average precision mean (mAP50) of the YOLOv8-ECW model for various targets is 92.4 %, an increase of 2.1 %; and the mAP50-95 is 75.4 %, an increase of 4.0 %. ② The detection speed of the YOLOv8-ECW is 238 frames per second, which is 5 frames per second higher than that of the YOLOv8n model. ③ Compared with the mainstream network models such as YOLOv6 and YOLOv7, the detection performance of the YOLOv8-ECW model is the best and it exhibits better robustness.