TBM rock fragmentation classification using an adaptive spot denoising and contour-texture decomposition attention-based method

Abstract

The particle size and morphology of the rock slag on the TBM conveyor belt are important for the driver’s adjustment of the tunneling parameters. However, the light source at the shooting site produces strong light pollution on the rock image, which greatly affects the image recognition results. This paper proposes an image adaptive spot denoising combined with contour-texture decomposition attention-based method for TBM rock fragmentation classification (AD-CDAN). The proposed method first calculates the global threshold from the grayscale distribution, performs image threshold segmentation to achieve light noise region localization, and then locally fills the texture after image Gaussian smoothing to achieve adaptive denoising. Then, the denoised image is fed into the decomposition attention block, where contour-texture decomposition is carried out for each feature map, and an exactor is presented to optimize the decomposition effect by mapping the decomposition hyperparameters from the input. Meanwhile, the normalized channel attention is computed from the input to output the feature maps with weights. Next, the output results are summed with the features obtained after downsampling the input image, and feedforward processing is performed to obtain the output of a single decomposition attention block. Finally, multiple decomposed attention blocks are stacked and the final extracted image contour features are linearly classified. In addition, this paper proposes a method to add light spot noise to simulate the harsh environment that may occur at the TBM construction site. Experimental validations are conducted on two datasets from the Baolin Tunnel project in Hubei Province and one dataset from Sichuan-Tibet Railway. The results show that the average accuracy of AD-CDAN in the two datasets of Baolin Tunnel without additional noise exceed 93%, and exceed 87% in the two datasets-noised. In the dataset from Sichuan-Tibet Railway, the accuracy of AD-CDAN still exceeds 85%. All results show that the accuracy of AD-CDAN exceeds the comparative models by 2.38%-42.87%, which verifies the effectiveness, superiority, and strong robustness of the proposed AD-CDAN, indicating that the method can be adapted to harsher working environments and provide important support for the safe tunneling of TBM.