煤矿井下胶带运输图像具有照度低、细节不清晰、背景干扰等特点，现有的带式输送机异物检测模型存在精度低、灵活性差、计算量大、优化空间存在差异等问题。针对上述问题，提出了一种基于生成对抗网络（GAN）的带式输送机异物检测方法。对胶带运输过程视频文件进行预处理，分类得到正常图像、异常图像，制作实验数据集对改进GANomaly模型进行训练，再通过训练好的模型进行带式输送机异物检测。在训练阶段，将不含异物的带式输送机图像作为输入；在测试阶段，将含有异物的带式输送机图像作为输入，得到的重构图像与输入网络的原图像作差，即可得到异物的具体位置。GANomaly模型轻量化改进方法：在GANomaly基础网络模型中加入深度可分离卷积残差模块，采用深度可分离卷积代替原有主干网络中的卷积操作，大幅降低了模型计算量，同时减少了参数的冗余计算，能够明显提高异物检测速度；通过合并多个批量归一化（BN）层，加快模型的收敛迭代速度，提高模型的泛化收敛能力，有效避免梯度消失。实验结果表明，改进GANomaly模型相较于传统GANomaly模型，在运行速度上提升了6.27%，评价指标F1分数、AUC、召回率（Recall）和平均精度均值（mAP）分别提升了19.05%，22.22%，15.00%，17.14%。

The images of coal mine underground belt transportation have the features of low illumination, unclear details, and background interference. The existing foreign object detection models for belt conveyors have problems such as low precision, poor flexibility, large computational complexity, and differences in optimization space. In order to solve the above problems, a foreign object detection method for belt conveyors based on generative adversarial nets (GAN) is proposed. The method preprocesses the video files of the tape transportation process, classifies them into normal and abnormal images. The method creats an experimental dataset to train the improved GANomaly model, and then uses the trained model to detect foreign objects in the belt conveyor. During the training phase, the image of the belt conveyor without foreign objects is used as input. In the testing phase, the image of the belt conveyor containing foreign objects is used as input. The reconstructed image obtained is subtracted from the original image of the input network to obtain the specific position of the foreign object. The lightweight improvement method of GANomaly model adds a depthwise separable convolution residual module to the GANomaly basic network model, and uses depthwise separable convolution to replace the convolution operation in the original backbone network. It greatly reduces the computational complexity of the model and reduces the redundant calculation of parameters, which can significantly improve the speed of foreign object detection. By merging multiple batch normalization (BN) layers, the convergence iteration speed of the model is accelerated, the generalization convergence capability of the model is improved, and gradient vanishing is effectively avoided. The experimental results show that the improved GANomaly model has improved the running speed by 6.27% compared to the traditional GANomaly model. The evaluation indicators F1 score, AUC, Recall, and mean average precision (mAP) have increased by 19.05%, 22.22%, 15.00%, and 17.14%, respectively.