基于深度生成模型的煤矿运输皮带异物检测

Foreign Object Detection of Coal Mine Belt Conveyor Based on Deep Generative Model

为了能够精准地对煤矿皮带运输机上的异物进行检出,提出了一种基于深度生成模型的皮带异物检测方法.首先,利用常规的变分自编码器(variational autoencoder,VAE)对图像进行重构,根据原始图像与重构图像之间的重构误差对图像中是否存在异物进行检出.然后,为了解决变分自编码器所生成的重构图像通常较为模糊的问题,引入了生成式对抗网络(generative adversarial network,GAN),对原始图像和重构图像进行判断,获取更加清晰的重构图像,以便提升异物检测精度.最后,将变分自编码器与生成式对抗网络进行结合,设计一种适用于皮带异物检测的深度学习算法.实验结果表明,与基线方法对比,本文方法在各评价指标上均有较好的效果.

A foreign object detection method based on the deep generative model is proposed to accurately detect the foreign objects on the coal mine belt conveyor.First,a conventional variational autoencoder(VAE)is used to reconstruct the image,and the presence of foreign objects in the image is detected according to the reconstruction error between the original image and the reconstructed image.Considering that the reconstructed image generated by the VAE is usually fuzzy,a generative adversarial network(GAN)is introduced to evaluate the original image and the reconstructed image for a clearer image and higher foreign object detection accuracy.Finally,the VAE is combined with the GAN to design a deep learning algorithm suitable for belt foreign object detection.The experimental results show that compared with the baseline method the proposed method has a better effect on every evaluation indexes.