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.

中文翻译：

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LithoSegNet：基于区域注意力的多尺度和跨阶段特征深度融合，用于实时岩性分割


岩性识别在工程建设、灾害预测、油藏评价等领域发挥着重要作用。然而，随着机械化、自动化的发展，传统的岩性识别方法在识别速度和效率上逐渐无法满足现有需求。为了实现实时、快速、准确的岩性识别，我们设计了轻量级模型主干，提出了基于区域注意力的多尺度特征提取模块和跨阶段特征融合模块，开发了逐步融合方法针对多阶段特征方案，开发了实时岩性分割模型。实验结果表明，特征提取模块将模型的mIoU提高了0.0021；跨阶段特征融合模块将模型的mIoU提高了0.0104；逐步融合方案可以有效地融合浅层空间特征和深层语义特征。使用该方案的模型mIoU分别比使用SegFormer和SegNeXt融合方案的模型mIoU高0.0246和0.0145。我们的模型 LithoSegNet 可以达到 0.9583 的 mIoU，速度为每秒 116.25 张图像，全面超越了许多最先进的模型。研究成果可为野外勘探和现场施工中的自动化地质绘制提供技术支撑，为地下工程智能化施工奠定基础，具有重要的科学和工程应用价值。