To solve the problem of conveyor belt tear detection in the special operating environment of underground mines, a lightweight detection algorithm based on line laser assistance and improved YOLOv7 is proposed. Firstly, considering that the conveyor belt tear is mainly small targets, the largest detection layer is not needed, thus simplifying the network model to reduce the model size and the number of parameters. In addition, the dynamic nonmonotonic FM-based Wise-IoU loss function is adopted to make the model pay more attention to common quality samples and improve the model detection performance. Then, the LAMP pruning method is used to improve the model′s computing speed and reduce the computing complexity, achieving the lightweight of the detection network. The channel knowledge distillation is used to improve the model accuracy without loss, and finally, the model is accelerated by TensorRT to achieve faster detection speed. The experimental results show that compared with the benchmark model, the improved model has a parameter number and computing volume reduced by 86.8% and 49.2%, respectively, mAP@0.5:0.95 reached 62.4%, and the detection speed was improved by 151.0 fps, the model size was reduced from 71.3 MB to 12.8 MB. After the improvement, the model has improved the accuracy and real-time detection of conveyor belt tear faults.