A foreign object detection method for coal conveyor belts based on brightness self-balancing and multi-scale feature fusion

Abstract In coal mine belt conveying systems, foreign objects falling may cause safety risks, such as conveyor belt tearing or blockage. To address these challenges in complex underground environments, a foreign object detection method for coal conveyor belts based on brightness self-balancing and multi-scale feature fusion called BS-MFFNet is introduced. First, to mitigate the impact of poor lighting and coal dust interference, an environmental perception dynamic enhancer is designed, thus improving image quality. Second, to enhance the detection accuracy of irregular targets, deformable convolutions are integrated into the backbone network. Subsequently, to further enhance feature representation capabilities, a multi-scale feature fusion network is developed, integrating both global and local information to provide richer target features. Lastly, an adaptive kernel-aware attention module is incorporated into the detection head to enhance target representation in complex, low-illumination backgrounds. Experimental results show that BS-MFFNet outperforms eight classical algorithms, achieving an average accuracy of 97.4%, an F 1 score of 0.93, and a detection speed of 74.6 fps in environments with weak lighting and high coal dust concentrations.