Study on Lightweight Algorithm for Multi-Scale Target Detection of Personnel and Equipment in Open Pit Mine

Personnel and equipment target detection algorithms in open pit mines have significantly improved mining safety, production efficiency, and management optimization. However, achieving precise target localization in complex backgrounds, addressing mutual occlusion among multiple targets, and detecting large-scale and spatially extensive targets remain challenges for current target detection algorithms in open pit mining areas. To address these issues, this study proposes a novel target detection algorithm named RSLH-YOLO, specifically designed for personnel and equipment detection in complex open pit mining scenarios. Based on the YOLOv11 (You Only Look Once version 11) framework, the algorithm enhances the backbone network by introducing receptive field attention convolution and dilated convolution to expand the model’s receptive field and reduce information loss, thereby improving target localization capability in complex environments. Additionally, a bidirectional fusion mechanism between high-resolution and low-resolution features is adopted, along with a dedicated small-target detection layer, to strengthen multi-scale target recognition. Finally, a lightweight detection head is implemented to reduce model parameters and computational costs while improving occlusion handling, making the model more suitable for personnel and vehicle detection in mining environments. Experimental results demonstrate that RSLH-YOLO achieves a mAP (mean average precision) of 89.1%, surpassing the baseline model by 3.2 percentage points while maintaining detection efficiency. These findings indicate that the proposed model is applicable to open pit mining scenarios with limited computational resources, providing effective technical support for personnel and equipment detection in mining operations.