Conveyor-Belt Detection of Conditional Deep Convolutional Generative Adversarial Network

Abstract

In underground mining, the belt is a critical component, as its state directly affects the safe and stable operation of the conveyor. Most of the existing non-contact detection methods based on machine vision can only detect a single type of damage and they require pre-processing operations. This tends to cause a large amount of calculation and low detection precision.To solve these problems, in the work described in this paper a belt tear detection method based on a multi-class conditional deep convolutional generative adversarial network (CDCGAN) was designed. In the traditional DCGAN, the image generated by the generator has a certain degree of randomness. Here, a small number of labeled belt images are taken as conditions and added them to the generator and discriminator, so the generator can generate images with the characteristics of belt damage under the aforementioned conditions. Moreover, because the discriminator cannot identify multiple types of damage, the multi-class softmax function is used as the output function of the discriminator to output a vector of class probabilities, and it can accurately classify cracks, scratches, and tears. To avoid the features learned incompletely, skiplayer connection is adopted in the generator and discriminator. This not only can minimize the loss of features, but also improves the convergence speed. Compared with other algorithms, experimental results show that the loss value of the generator and discriminator is the least. Moreover, its convergence speed is faster, and the mean average precision of the proposed algorithm is up to 96.2%, which is at least 6% higher than that of other algorithms.

Damage Detection for Conveyor Belt Surface Based on Conditional Cycle Generative Adversarial Network

“…Yu et al [30] proposed a novel SeqGAN to address the problem of the poor performance of the original GAN for generating sequential data. For damaged belt surface detection, an improved GAN model was proposed in [31] based on deep convolutional GAN (DCGAN) with labels embedded in latent layer and multi-class Softmax as activation function. In addition, skip connection is used in both generator and discriminator networks, which can alleviate the vanishing gradient problem and improve training speed.…”

Solar Image Cloud Removal based on Improved Pix2Pix Network

“…The two play a dynamic game to generate data similar to the distribution of training data. The proposal of wasserstein generative adversarial network (WGAN) [13], conditional generative adversarial network (CGAN) [14] and deep convolutional generative adversarial network (DCGAN) [15] has gradually improved the training instability of the GAN model, and the GAN model has been used in an increasing number of image processing tasks, including image recognition [16], multi-class detection [17], style transfer [18] and super resolution [19].…”