基于生成对抗网络的带式输送机异物检测方法

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.