Abstract: The object detection algorithm based on deep learning has good recognition performance in foreign object detection. But the model memory requirement is large and the detection speed is slow. The lightweight deep learning networks can significantly reduce model memory requirements and improve detection speed. But their detection precision is low in weak light environments underground. In order to solve the above problems, a real-time foreign object detection algorithm for belt conveyors based on Faster-YOLOv7 is proposed. By using the contrast limited adaptive histogram equalization (CLAHE) with limited contrast for image enhancement, the contrast of foreign objects in low light environments is improved. Lightweight design of the YOLOv7 backbone network based on Mobilenetv3 is carried out to reduce the computational and parameter load of the YOLOv7 model. By adding an effective channel attention mechanism, the method alleviates the problem of high-level feature information loss caused by a decrease in the number of feature channels. Alpha-IoU is used as the loss function to improve the precision of foreign object detection. The experimental results show the following points. ① The initial loss of Faster-YOLOv7 is 0.143, and the final stability is around 0.039. ② The detection speed of Faster-YOLOv7 can reach 42 frames/s, which is 17 and 20 frames/s higher than YOLOv5 and YOLOv7, respectively. Faster-YOLOv7 has a memory of 14 MiB, which is 29 and 57 MiB lower than YOLOv5 and YOLOv7, respectively. The detection accuracy reaches 91.3%, which is 8.8% higher than YOLOv5. ③Applying SSD, YOLOv5, lightweight YOLOv7, and Faster-YOLOv7 object detection algorithms to the coal conveying images and videos of underground belt conveyors in coal mines, it is found that SSD misses detection during video detection. YOLO series models effectively recognized the foreign objects to be tested, and Faster-YOLOv7 recognition results has a higher confidence level.