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

TL;DR

DIS-Mine is a novel instance segmentation method designed to identify disaster-affected areas in underground mines under low-light conditions, significantly aiding rescue efforts with improved accuracy and robustness.

Contribution

The paper introduces DIS-Mine, a new instance segmentation approach tailored for poor-light underground mines, and provides a new dataset, ImageMine, for realistic evaluation.

Findings

Achieves an F1 score of 86.0% on ImageMine

Outperforms state-of-the-art methods by at least 15x in performance

Provides up to 80% higher precision in object detection

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.