Abstract: For the problem of interference between shearer drum and hydraulic support guard plate, an interference state intelligent recognition method for shearer drum and hydraulic support guard plate of improved YOLOv5s algorithm is proposed. The use of boundary constraint and non-linear contextual regularization based on the group's previous proposed method of defogging and dust removal to clarify the video image, improve the quality of the monitoring video image of the comprehensive mining face. The YOLOv5s model is improved by replacing the ordinary convolutional Conv in the YOLOv5s backbone network with Ghost convolution, the improved algorithm greatly reduces the number of model parameters and improves the model recognition speed. At the same time, the coordinate attention mechanism is introduced to improve the model's ability to extract the features from the guard plate and shearer, and improve the recognition accuracy. The soft non-maximum suppression algorithm (Soft-NMS) anchor frame screening method is used to reduce the problem of missed detection due to overlapping guard plates. For the problem of determining the interference state of shearer drum and hydraulic support guard plate, the method for determining anchor box overlap degree between hydraulic support guard plate and shearer drum. The improved YOLOv5s algorithm is compared with YOLOv5s and YOLOv3-tiny algorithm. The results indicate that compared with the original YOLOv5s model, the recognition accuracy of this method has been improved by about 8.1%, and GFLOPs have been reduced by 1.86 times. mAP@.5 was increased to 97.2%, and the average recognition speed is 169 frames/s. The improved YOLOv5s algorithm is used to validate the interference state recognition effect for video images of shearer drum and hydraulic support guard plate in in actual fully mechanized mining faces, and the results show that the recognition accuracy of interference state between the coal shearer drum and the hydraulic support guard plate is 96%.