Aiming at the problem of low detection accuracy of conveyor belt defect detection of belt conveyor due to the lack of public data sets, the diversification of defect shapes and the different lengths of tearing, this paper will use linear array camera and use linear laser as an auxiliary tool in the shooting process to reduce the influence of harsh environment on the image, and put forward an improved YOLOv5 conveyor belt defect detection algorithm to ensure the production safety of coal mine. Firstly, on the basis of the existing data, the method of combining multiple data enhancement methods is extended. Then, in the feature extraction stage, the C3 module in Backbone is replaced with a C3_A similar to the attention mechanism to improve the overall performance. Then, in the feature fusion stage, the short-circuit method is used to combine the PAN structure of Backbone and Neck to reduce the loss of feature information. Finally, the fine-tuned intersection-union ratio is integrated into the loss function and two parameters are set. The original intersection-union ratio is scaled and cropped, which effectively constrains the position relationship between the model prediction box and the real box, and further improves the accuracy of the model 's boundary box regression. The experimental results show that the average accuracy of conveyor belt defect detection is 88.1%, the accuracy rate is 88%, and the recall rate is 86.5%, which meets the detection requirements of conveyor belt defects.