改进YOLOv8s与DeepSORT的矿工帽带检测及人员跟踪OA北大核心CSTPCD

Ensuring proper safety helmet usage is of utmost importance in underground mining inspections to protect workers.However,challenging conditions,such as high temperatures,often lead to non-compliant helmet wearing behavior.Existing detection methods are insufficient for underground environments,resulting in low recognition accuracy and inad-equate detection of correctly worn helmets.To address these issues,this paper proposes an improved version of the CM-YOLOv8s algorithm that focuses on the chin strap as a small target for safety helmet detection and compliance assess-ment.The DeepSORT algorithm is then employed to track workers who fail to comply with helmet-wearing regulations.To begin,a comprehensive dataset is curated utilizing underground surveillance cameras.The CM-YOLOv8s algorithm is leveraged for safety helmet detection by incorporating higher-resolution feature maps and introducing a cascaded query mechanism.This approach enables precise detection of small targets without significantly increasing computational costs.Furthermore,the enhanced DeepSORT algorithm is employed for person tracking by replacing the small residual network in DeepSORT with deeper convolutional layers,thereby enhancing the extraction of appearance information.The pro-posed algorithm is validated using a self-made dataset for underground safety helmet detection and tracking.Experimental results demonstrate that CM-YOLOv8s achieves an average precision of 92.3% for safety helmet recognition,which is a 4.2 percentage points improvement over YOLOv8s.Additionally,the average accuracy of the safety helmet compliance recognition system,based on CM-YOLOv8s and DeepSORT,is 85.37% ,with a detection speed of 59 FPS.The proposed algorithm effectively addresses compliance detection in safety helmet wearing by accurately assessing the position of the chin strap in proximity to the individual's jaw.It strikes an optimal balance between detection speed and accuracy while exhibiting robust adaptability to the complex underground environments.The successful implementation of this algorithm at the Chensilou Coal Mine over an extended period has demonstrated its efficacy in monitoring and providing early warnings for abnormal safety helmet wearing,thereby bolstering regulatory oversight and promoting the compliant use of safety helmets among miners.The algorithm holds great potential for enhancing safety measures in underground mining inspections and can be applied to similar industrial scenarios.Further research and development in this direction are warranted to expand its applicability and impact.