Longitudinal tear detection of conveyor belts is one of the important issues in coal mine safety production. In the longitudinal tear detection of mining conveyor belts, insufficient detection accuracy, false detections, and missing detections occur due to insufficient data, diversified damage patterns, and extreme aspect ratios. In this study, an improved YOLOv4 longitudinal tear detection algorithm for conveyor belts is proposed. First, the existing data is expanded by data enhancement to construct a longitudinal tear data set for conveyor belts. Secondly, the variable convolution is added to the backbone network to enhance the feature extraction ability of the model for diverse damage patterns. Finally, in the feature fusion stage, the cross-stage partial network (CSPNet) structure is introduced to improve the longitudinal tear detection performance of the model for extreme aspect ratios, and further reduce missing detection and false detection. The experimental results show that the accuracy of the longitudinal tear detection for the conveyor belt reaches 92.5%, and the F1 score reaches 93.1%, which basically meets the requirements of the longitudinal tear detection for the conveyor belt.