基于深度学习的目标检测算法在异物检测中具有较好的识别效果，但模型内存需求大，检测速度慢；轻量化深度学习网络能够大幅减少模型内存需求，提升检测速度，但在井下弱光环境中检测精度低。针对上述问题，提出了一种基于Faster−YOLOv7的带式输送机异物实时检测算法。通过限制对比度自适应直方图均衡化算法（CLAHE）进行图像增强，提高弱光环境中异物对比度；基于Mobilenetv3对YOLOv7主干网络进行轻量化设计，减少YOLOv7模型的计算量、参数量；添加有效通道注意力机制，缓解因特征通道数减少而导致的高层特征信息丢失问题；采用Alpha−IoU作为损失函数提高异物检测精度。实验结果表明：① Faster−YOLOv7的初始损失为0.143，最终稳定在0.039左右。② Faster−YOLOv7的检测速度可达42帧/s，较YOLOv5、YOLOv7分别提升了17，20帧/s；Faster−YOLOv7内存为14 MiB，较YOLOv5、YOLOv7分别降低了29，57 MiB；检测准确率达91.3%，较YOLOv5提升了8.8%。③ 将SSD、YOLOv5、轻量化YOLOv7、Faster−YOLOv7目标检测算法应用到煤矿井下带式输送机运煤图像及视频中，发现SSD在视频检测时发生了漏检现象，YOLO系列模型均有效地识别出待测异物，且Faster−YOLOv7识别结果的置信度更高。

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.