A real-time detection for miner behavior via DYS-YOLOv8n model

To address the issues of low real-time performance and poor algorithm accuracy in detecting miner behavior underground, we propose a high-precision real-time detection method named DSY-YOLOv8n based on the characteristics of human body behavior. This method integrates DSConv into the backbone network to enhance multi-scale feature extraction. Additionally, SCConv-C2f replaces C2f modules, reducing redundant calculations and improving model training speed. The optimization strategy of the loss function is employed, and MPDIoU is used to improve the model’s accuracy and speed. The experimental results show: (1) With almost no increase in parameters and calculation amount, the mAP50 of the DSY-YOLOv8n model is 97.4%, which is a 3.2% great improvement over the YOLOv8n model. (2) Compared to Faster-R-CNN, YOLOv5s, and YOLOv7, DYS-YOLOv8n has improved the average accuracy to varying degrees while significantly increasing the detection speed. (3) DYS-YOLOv8n meets the real-time requirements for behavioral detection in mines with a detection speed of 243FPS. In summary, the DYS-YOLOv8n offers a real-time, efficient, and lightweight method for detecting miner behavior in mines, which has high practical value.