Article, 2024

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

Expert Systems with Applications, ISSN 0957-4174, 1873-6793, Volume 249, Page 123628, 10.1016/j.eswa.2024.123628

Contributors

Yang, Yukun 0000-0002-2123-9999 [1] Zhou, Wei 0000-0002-8758-2511 (Corresponding author) [1] Jiskani, Izhar Mithal 0000-0002-3220-8880 [2] Wang, Zhiming [1]

Affiliations

  1. [1] China University of Mining and Technology
    2. [NORA names: China; Asia, East];
  2. [2] Aalborg University
    3. [NORA names: AAU Aalborg University; University; Denmark; Europe, EU; Nordic; OECD]

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.

Keywords

Contrast Limited Adaptive Histogram Equalization, DeepLab, DeepLab v3+, FCN, PSPNet, UNet, V3+, ablation, ablation experiments, adaptive histogram equalization, alterations, applications, area, attention, automation, channel attention, computer, computer vision, consequences, contrast, dataset, driving, dynamic nature, edge, effect, efficiency, efficient channel attention, environment, equality, evaluation, evaluation metrics, experiments, frequent alterations, function, histogram equalization, hybrid, hybrid loss function, indistinct edges, industry, intelligence, intelligent mining, loss function, mechanism, method, metrics, mine road, mining, mining area, mining environment, mining industry, model, modulation, nature, network, network model, open pit, open pit mine, operating environment, perspective, production, production progress, production task, progression, residual network, results, road, road dataset, road segmentation method, roadway, rock types, rocks, segmentation method, segmentation network, semantic segmentation network, semi-enclosed, smart mines, study, task, truck, type, unmanned driving, unmanned trucks, unmanned vehicles, validity, vehicle, vision

Funders

  • National Natural Science Foundation of China

Data Provider: Digital Science

LINKS
-

Matching Records in NORA

SUBJECTS
+

METRICS
+