Extracting unstructured roads for smart Open-Pit mines based on computer vision: Implications for intelligent mining

Abstract

The mining industry is rapidly advancing towards automation and intelligence, with smart mines emerging as a future trend. Open-pit mining areas are semi-enclosed, and roads are essential for unmanned trucks to perceive the mining environment and execute various production tasks. The dynamic nature of open-pit mines, driven by production progress, leads to frequent alterations in roadways. As a consequence, roads become unstructured, with indistinct edges that easily blend into the surrounding mine environment. This poses a challenging operational environment for unmanned vehicles. To address this challenge in the realm of intelligent mining, this study establishes a dataset of mining roads based on different rock types and proposes an unstructured road segmentation method for mines by integrating residual networks, Contrast Limited Adaptive Histogram Equalization (CLAHE), and the Efficient Channel Attention (ECA) mechanism. This method is applied to four semantic segmentation networks: FCN, UNet, PSPNet, and DeepLab v3 +. The dataset and network model undergo validation using a specific hybrid loss function and relevant evaluation metrics. The results show that the established road dataset has good applicability, with an ablation experiment confirming the effectiveness of the added modules. This study introduces a new perspective for advancing unmanned driving in smart mines.