Abstract: Due to the complex working conditions of high noise, low illumination, and blurred movement in coal mines underground, as well as the phenomenon of coal gangue easily gathering, it is difficult to extract features from coal gangue object detection models. The classification and positioning of coal gangue are inaccurate. In order to solve the above problems, a coal gangue recognition method based on the CED-YOLOv5s model is proposed. Firstly, the coordinate attention (CA) mechanism is introduced into the YOLOv5s backbone network, which encodes feature maps by embedding coordinate information into channel relationships and long-range dependencies. The method fully utilizes channel attention information and spatial attention information to make the model focus more on important features and suppress irrelevant information. Secondly, the EIoU regression loss function is introduced in the detection head of YOLOv5s to minimize the width and height difference between the object box and anchor box. It enhances the position and boundary information of the object, improves the positioning precision and convergence speed of the model in dense objects. Finally, a lightweight decoupling head is introduced in the detection head of YOLOv5s, decoupling separate feature channels for classification and regression tasks. It solves the interference problem between the coupling head part of the class task and the regression task in the original model, further improving the parallel operation efficiency and detection precision of the model. The experimental results show that the CED-YOLOv5s model has the best overall performance compared to other YOLO series object detection models. It has an average detection precision of 94.8%, an improvement of 3.1% compared to the YOLOv5s model, and a detection speed of 84.8 frames/s. The results can fully meet the real-time detection requirements of coal gangue in coal mines.