Real-time semantic segmentation for underground mine tunnel

Abstract

Semantic segmentation is the underlying technology for many intelligent applications in underground mines. Unlike the ordinary scenario, the lighting of underground mines varies drastically, and there is heavy dust and mist, which results in poor image quality and greatly hinders the application of image semantic segmentation in underground mines. Because there is currently no datasets for underground mine tunnels available, a dataset named Underground Mine Tunnel Semantic Segmentation Dataset (UMTSSD) have to be constructed to support our research. UMTSSD consists of 3461 meticulously annotated images and 17 annotated categories. A real-time semantic segmentation algorithm named Fast Adaptive Deep Dual-resolution Network (FA-DDRNet) which uses Deep Dual-resolution Network (DDRNet) as the backbone is proposed for underground mines. To enhance the semantic segmentation accuracy in underground environment, FA-DDRNet introduces two modules: Fast Adaptive Input Normalization Module (FAINM) and Scale-wise Residual Cascade Module (SRCM). FAINM can autonomously and quickly adjust normal lighting images, weak lighting images, and overexposed images to improve the robustness of semantic segmentation algorithms. SRCM is integrated into the backbone to swiftly fuse multi-scale features in a cascade fashion, resulting in enhanced detection of objects with diverse shapes in underground environment. Finally, our method achieves exceptional performance with a superior inference speed compared to other semantic segmentation algorithms in UMTSSD. The method can realize running in real-time on low computational power embedded devices, which is well adapted to underground environment.