Precise identification of ground fissures is of paramount importance for the safety and environmental management of coal mining areas. However, the surface environment in coal mining regions is complex, and, to date, the efficiency of artificial fissure detection has been relatively low. Therefore, we have proposed a ground fissure automatic identification model based on an encoder–decoder architecture known as the Ground Fissure Segmentation Network (GFSegNet). The encoder adopts a deep-shallow decoupled mode. The shallow network achieves spatial and spectral domain interaction by introducing adaptive Fourier convolution. The deep network adopts a hierarchical Transformer with an efficient self-attention mechanism for global modeling of fine-grained semantics. The decoder is designed as a multi-scale feature fusion structure embedded in pyramid pooling modules, aiming to efficiently utilize multi-scale ground fissure information. To advance the application of deep learning in ground fissure identification, we created a coal mining area ground fissure segmentation dataset from drone imagery, known as the mine ground fissure unmanned aerial vehicle dataset (MGF-UAV). On this dataset, the overall performance of GFSegNet surpasses the current leading segmentation models, and its reliability and generalization capabilities are further validated on additional datasets (Crack500, DeepCrack, CrackForest and ISPRS-Postdam). This research has brought expansion and innovation to the field of automatic ground fissure recognition in coal mining areas, offering new perspectives and methodologies for the application of deep learning techniques in this domain.

中文翻译：

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GFSegNet：矿区地裂缝多尺度分割模型

地裂缝的精确识别对于煤矿区的安全和环境管理至关重要。但煤矿区地表环境复杂，目前人工裂隙检测效率较低。因此，我们提出了一种基于编码器-解码器架构的地裂缝自动识别模型，称为地裂缝分割网络（GFSegNet）。编码器采用深浅解耦方式。浅层网络通过引入自适应傅立叶卷积实现空间域和谱域交互。深层网络采用具有高效自注意力机制的分层 Transformer，用于细粒度语义的全局建模。该解码器被设计为嵌入金字塔池化模块的多尺度特征融合结构，旨在有效利用多尺度地裂缝信息。为了推进深度学习在地裂缝识别中的应用，我们利用无人机图像创建了煤矿区地裂缝分割数据集，称为矿井地裂缝无人机数据集（MGF-UAV）。在此数据集上，GFSegNet的整体性能超越了当前领先的分割模型，其可靠性和泛化能力在其他数据集（Crack500、DeepCrack、CrackForest和ISPRS-Postdam）上得到了进一步验证。该研究为煤矿区地裂缝自动识别领域带来了拓展和创新，为深度学习技术在该领域的应用提供了新的视角和方法论。