An Automatic Pixel-Level Segmentation Method for Coal-Crack CT Images Based on U2-Net

Automatically segmenting coal cracks in CT images is crucial for 3D reconstruction and the physical properties of mines. This paper proposes an automatic pixel-level deep learning method called Attention Double U2-Net to enhance the segmentation accuracy of coal cracks in CT images. Due to the lack of public datasets of coal CT images, a pixel-level labeled coal crack dataset is first established through industrial CT scanning experiments and post-processing. Then, the proposed method utilizes a Double Residual U-Block structure (DRSU) based on U2-Net to improve feature extraction and fusion capabilities. Moreover, an attention mechanism module is proposed, which is called Atrous Asymmetric Fusion Non-Local Block (AAFNB). The AAFNB module is based on the idea of Asymmetric Non-Local, which enables the collection of global information to enhance the segmentation results. Compared with previous state-of-the-art models, the proposed Attention Double U2-Net method exhibits better performance over the coal crack CT image dataset in various evaluation metrics such as PA, mPA, MIoU, IoU, Precision, Recall, and Dice scores. The crack segmentation results obtained from this method are more accurate and efficient, which provides experimental data and theoretical support to the field of CBM exploration and damage of coal.