LithoSegNet: Regional attention-based deep fusion of multi-scale and cross-stage features for real-time lithology segmentation

Abstract

Lithology identification plays an important role in engineering construction, disaster prediction, reservoir evaluation, and other fields. However, with the development of mechanization and automation, traditional lithology recognition methods are gradually unable to meet existing demands in terms of recognition speed and efficiency. In order to achieve real-time, fast, and accurate identification of lithology, we designed a lightweight model backbone, proposed a multi-scale feature extraction module and a cross-stage feature fusion module based on regional attention, developed a step-wise fusion scheme for multi-stage feature, and developed a real-time lithology segmentation model. The experimental results show that the feature extraction module improves the mIoU of the model by 0.0021; The cross-stage feature fusion module improves the mIoU of the model by 0.0104; The step-wise fusion scheme can effectively fuse shallow spatial features and deep semantic features. The model mIoU using this scheme is 0.0246 and 0.0145 higher than that using SegFormer and SegNeXt fusion schemes, respectively. Our model LithoSegNet can achieve a mIoU of 0.9583 with a speed of 116.25 images per second, comprehensively surpassing many state-of-the-art models. The research results can provide technical support for automated geological sketching in field exploration and on-site construction, and lay the foundation for intelligent construction of underground engineering, which has important scientific and engineering application value.