To address the problem that coal and gangue have similar appearances under complex working conditions, and that illumination fluctuation, motion blur, and occlusion easily lead to low detection accuracy as well as high false detection and missed detection rates, a coal gangue object detection method based on ECA-DEIM is proposed. In the backbone network, a lightweight multi-scale attention module is introduced to enhance the capture capability for multi-scale targets. A coal-gangue spatial refinement and selective correction module is constructed to improve the spatial representation capability of similar targets. An adaptive dynamic normalization module is designed to enhance the generalization ability of the model. Experimental results show that, on a self-built dataset, the mAP@［0.5:0.95］ and mAP@0.5 reach 52.3% and 82.5%, respectively, which are 2.2% and 2.4% higher than those of DEIM, while the number of parameters is reduced by 19.9%. The missed detection rate is reduced to 3.95%, the false detection rate is decreased by 4.3%, and the sorting accuracy reaches 93.1%. The results indicate that the proposed method has good detection accuracy, robustness, and engineering applicability under complex working conditions.