GFSegNet: A multi-scale segmentation model for mining area ground fissures

Abstract

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.

Keywords

• Ground fissures extraction
• Semantic segmentation
• Adaptive Fourier convolution
• Hierarchical transformer
• High-resolution UAV images
• Coal mining areas