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DIS-Mine: Instance Segmentation for Disaster-Awareness in Poor-Light Condition in Underground Mines

Mizanur Rahman Jewel, Mohamed Elmahallawy, Sanjay Madria, Samuel Frimpong

Abstract

Detecting disasters in underground mining, such as explosions and structural damage, has been a persistent challenge over the years. This problem is compounded for first responders, who often have no clear information about the extent or nature of the damage within the mine. The poor-light or even total darkness inside the mines makes rescue efforts incredibly difficult, leading to a tragic loss of life. In this paper, we propose a novel instance segmentation method called DIS-Mine, specifically designed to identify disaster-affected areas within underground mines under low-light or poor visibility conditions, aiding first responders in rescue efforts. DIS-Mine is capable of detecting objects in images, even in complete darkness, by addressing challenges such as high noise, color distortions, and reduced contrast. The key innovations of DIS-Mine are built upon four core components: i) Image brightness improvement, ii) Instance segmentation with SAM integration, iii) Mask R-CNN-based segmentation, and iv) Mask alignment with feature matching. On top of that, we have collected real-world images from an experimental underground mine, introducing a new dataset named ImageMine, specifically gathered in low-visibility conditions. This dataset serves to validate the performance of DIS-Mine in realistic, challenging environments. Our comprehensive experiments on the ImageMine dataset, as well as on various other datasets demonstrate that DIS-Mine achieves a superior F1 score of 86.0% and mIoU of 72.0%, outperforming state-of-the-art instance segmentation methods, with at least 15x improvement and up to 80% higher precision in object detection.

DIS-Mine: Instance Segmentation for Disaster-Awareness in Poor-Light Condition in Underground Mines

Abstract

Detecting disasters in underground mining, such as explosions and structural damage, has been a persistent challenge over the years. This problem is compounded for first responders, who often have no clear information about the extent or nature of the damage within the mine. The poor-light or even total darkness inside the mines makes rescue efforts incredibly difficult, leading to a tragic loss of life. In this paper, we propose a novel instance segmentation method called DIS-Mine, specifically designed to identify disaster-affected areas within underground mines under low-light or poor visibility conditions, aiding first responders in rescue efforts. DIS-Mine is capable of detecting objects in images, even in complete darkness, by addressing challenges such as high noise, color distortions, and reduced contrast. The key innovations of DIS-Mine are built upon four core components: i) Image brightness improvement, ii) Instance segmentation with SAM integration, iii) Mask R-CNN-based segmentation, and iv) Mask alignment with feature matching. On top of that, we have collected real-world images from an experimental underground mine, introducing a new dataset named ImageMine, specifically gathered in low-visibility conditions. This dataset serves to validate the performance of DIS-Mine in realistic, challenging environments. Our comprehensive experiments on the ImageMine dataset, as well as on various other datasets demonstrate that DIS-Mine achieves a superior F1 score of 86.0% and mIoU of 72.0%, outperforming state-of-the-art instance segmentation methods, with at least 15x improvement and up to 80% higher precision in object detection.

Paper Structure

This paper contains 16 sections, 6 equations, 7 figures, 3 tables, 4 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. ImageMine Dataset Construction
  4. Data collection
  5. Data Filtering
  6. Data annotation
  7. Proposed Methodology
  8. Image Brightness Improvement Component
  9. Instance Segmentation with SAM Integration Component
  10. Mask R-CNN-based Segmentation Component
  11. Mask Alignment with Feature Matching
  12. Experiments and Results
  13. Experimental setup
  14. Baselines
  15. Results
  16. ...and 1 more sections

Figures (7)

  • Figure 1: Samples from our underground mine dataset captured under extremely low-light conditions.
  • Figure 2: Construction process for our ImageMine dataset.
  • Figure 3: An overview of proposed DIS-Mine instance segmentation framework.
  • Figure 4: Illustration of the instance segmentation with SAM integration component.
  • Figure 5: Evaluation of DIS-Mine against baselines on various metrics on the ImageMine dataset.
  • ...and 2 more figures