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<p>Identifying rock mass instabilities is crucial in geotechnical monitoring, particularly in underground structures such as&#xa0;deep underground mines. Conventional techniques for identifying unstable rock blocks rely on visual and acoustic assessments, which lack automation and objectivity. Consequently, there is an increasing demand for sophisticated monitoring techniques that fulfill the precision and effectiveness standards for underground geotechnical assessment. This study will employ deep learning-based computer vision models to propose an intelligent approach for detecting and segmenting unstable rock blocks in underground mines. It utilizes three variants of the U-shaped convolutional neural network, referred to as U-Net: Base-U-Net, Attention U-Net, and BCDU-Net (Backbone Convolutional Deconvolution U-Net), to identify and segregate unstable rock blocks in thermal images. A detailed dataset of 1502 annotated images was assembled from a video recorded at the Draa Sfar deep underground mine in Morocco using a FLIR A70 Research &amp; Development thermal camera for model training, validation, and testing. In this work, the Base-U-Net model achieved high training and validation accuracies, as well as an average performance during the testing phase. The results demonstrate the effectiveness of the U-Net model in segmenting unstable rock blocks in thermal images, particularly as a first attempt to apply this model in the field of mining geotechnics. Our research highlights the use of deep learning-based computer vision models to enhance geotechnical engineering applications and safety protocols in mining structures.</p>

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A novel application of deep learning and thermal image segmentation to detect loosening rocks in the Draa Sfar deep underground mine in Morocco

  • 期刊论文

  • 发表日期：

    2025年8月5日

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  • Kaoutar Clero,
  • Said Ed-Diny,
  • Mohammed Achalhi,
  • Mouhamed Cherkaoui,
  • Imad El Harraki,
  • Hamd Ait Abdelali,
  • Sanaa El Fkihi,
  • Intissar Benzakour,
  • Said Rziki,
  • Hicham Tagemouati,
  • François Bourzeix,
  • Othmane Ed-Diny,
  • Fatima Zahra Chieh

摘要

Identifying rock mass instabilities is crucial in geotechnical monitoring, particularly in underground structures such as deep underground mines. Conventional techniques for identifying unstable rock blocks rely on visual and acoustic assessments, which lack automation and objectivity. Consequently, there is an increasing demand for sophisticated monitoring techniques that fulfill the precision and effectiveness standards for underground geotechnical assessment. This study will employ deep learning-based computer vision models to propose an intelligent approach for detecting and segmenting unstable rock blocks in underground mines. It utilizes three variants of the U-shaped convolutional neural network, referred to as U-Net: Base-U-Net, Attention U-Net, and BCDU-Net (Backbone Convolutional Deconvolution U-Net), to identify and segregate unstable rock blocks in thermal images. A detailed dataset of 1502 annotated images was assembled from a video recorded at the Draa Sfar deep underground mine in Morocco using a FLIR A70 Research & Development thermal camera for model training, validation, and testing. In this work, the Base-U-Net model achieved high training and validation accuracies, as well as an average performance during the testing phase. The results demonstrate the effectiveness of the U-Net model in segmenting unstable rock blocks in thermal images, particularly as a first attempt to apply this model in the field of mining geotechnics. Our research highlights the use of deep learning-based computer vision models to enhance geotechnical engineering applications and safety protocols in mining structures.