A novel framework for segmenting open-pit mining road

Accurate segmentation of open-pit mine road networks presents a critical challenge for mine digitization and autonomous driving applications. These roads are prone to mechanical compaction, geological erosion, and coverage by gravel dust, resulting in segmentation outcomes characterized by blurred boundaries, holes, fractures, and geometric deformations, which severely compromise measurement accuracy. To address these challenges, this paper proposes the Mining Road Segmentation Network (MRS-Net), which integrates local features with global semantics. First, a Residual Network Version 2 (ResNetV2)-Transformer cascaded encoder is constructed, employing residual connections to preserve sub-pixel-level edge details and multi-head self-attention to establish long-range dependencies, thereby enhancing the representation of weak texture features. Second, the Road Multi-scale Features Fusion Module (RMFF) was designed to extract local geometric features and global continuity features through progressive hollow convolution, enabling the model to extract multi-scale features and effectively suppress interference from gravel dust. Finally, a progressive decoding architecture incorporating bilinear interpolation is adopted to improve edge smoothness. MRS-Net is evaluated on an Unmanned Aerial Vehicle (UAV)-acquired road dataset from the Anshan open-pit iron mine in Liaoning Province, China. Results demonstrate that MRS-Net achieves superior segmentation performance compared to models such as DeepLabV3+ and TransUNet across three distinct scenarios: main roads, temporary roads, and abandoned roads. Specifically, it achieves Intersection over Union (IoU), Dice coefficient(Dice), and Kappa coefficient (Kappa) values of 89.4 % / 94.1 % / 87.2 %, 75.7 % / 83.3 % / 75.1 %, and 83.8 % / 90.0 % / 84.85 % respectively for these scenarios.