Research on Underground Coal Mine Object Detection Based on Image Enhancement and YOLOv11

Abstract

Underground coal-mine scenes are often affected by low lighting, dust, and motion blur., and monitoring images are often affected by low lighting, dust, and motion blur, which severely hinders the recognition performance of intelligent detection systems. To address the decline in object detection accuracy caused by poor image quality, this paper proposes an object detection method combining a detection-guided dual-branch image enhancement module with YOLOv11. The method includes two key innovations: first, a detection-guided image enhancement module is developed, which generates attention maps from low-confidence regions in YOLOv11’s detection output to guide the enhancement network in focusing on critical image areas, thereby improving detection accuracy. Second, a dual-branch structure combining brightness enhancement and detail reconstruction is designed, integrating the strengths of Zero-DCE and SRGAN to achieve global brightness correction and edge clarity enhancement. Experiments conducted on the publicly available underground coal mine image dataset DsLMF+ demonstrate that the proposed method outperforms traditional enhancement-plus-detection pipelines in multiple evaluation metrics. The enhancement module significantly improves image quality indicators, and achieves notable improvements in YOLOv11 detection accuracy, particularly in mean Average Precision (mAP). This study confirms the effectiveness of combining detection feedback and multi-scale enhancement in complex industrial environments, offering a promising solution for underground object detection tasks.