A Novel Denoising Approach Based on Improved Invertible Neural Networks for Real-Time Conveyor Belt Monitoring

Abstract

Powerful belt conveyor is the crucial transportation equipment for coal mining. Keeping its stable operation is essential to ensure the coal mining efficiency. At present, damage detection systems based on machine vision are widely studied and deployed. Clear images are the foundation for successful damage detection. However, in the harsh environment of underground coal seam, it is difficult to capture high-quality images, which limits the performance of the machine vision-based techniques for the conveyor belt damage detection. To solve this issue, an image denoising and enhancement approach is proposed based on the invertible neural network (INN). First, a new res-block is introduced to improve the feature extraction ability of the proposed model. Then, to take advantage of the INN architecture, input images are split by the generative flow (GLOW) coupling block in the forward path and the noise among the high-frequency (HF) features is discarded in the inverse path. An improved trainable guided filter (TGF) is proposed to reconstruct the HF images. Besides, an improved multiple loss function is designed to keep detailed feature information and eliminate the effect of noise. The comparisons and experimental results demonstrate that the proposed denoising INN (DeINN) presents excellent image denoising ability and outperforms several existing popular methods. Finally, a real-world application demonstrates that the proposed DeINN satisfies the industrial requirements on belt damage detection.