Yolov8-HAC: Safety Helmet Detection Model for Complex Underground Coal Mine Scene

The underground coal mine working environment is complicated, and the detection of safety helmet wearing is vital for assuring worker safety. This article proposes an improved YOLOv8n safety helmet detection model, YOLOv8-HAC, to address the issues of coexisting strong light exposure and low illumination, equipment occlusions that result in partial target loss, and the missed detection of small targets due to limited surveillance perspectives in underground coal mines. The model substitutes the suggested HAC-Net for the C2f module in YOLOv8n's backbone network to improve feature extraction and detection performance for targets with motion blur and low-resolution images. To improve detection stability in complicated situations and lessen background interference, the AGC-Block module is also included for dynamic feature selection. Additionally, a tiny target detection layer is included to increase the long-range identification rate of tiny safety helmets. According to experimental data, the enhanced model outperforms existing popular object detection algorithms, with a mAP of 94.8% and a recall rate of 90.4%. This demonstrates how well the suggested approach works to identify safety helmets in situations with complicated lighting and low-resolution photos.