arXiv subjects

Mizanur Rahman Jewel

Publications and source records attributed to Mizanur Rahman Jewel.

3 recordsLinked to original sources

arXiv2026

Future Mining: Learning for Safety and Security

Mining is rapidly evolving into an AI driven cyber physical ecosystem where safety and operational reliability depend on robust perception, trustworthy distributed intelligence, and continuous monitoring of miners and equipment. However, real world mining environments impose severe constraints, including poor illumination, GPS denied conditions, irregular underground topologies and intermittent connectivity. These factors degrade perception accuracy, disrupt situational awareness and weaken distributed learning systems. At the same time, emerging cyber physical threats such as backdoor triggers, sensor spoofing, label flipping attacks, and poisoned model updates further jeopardize operational safety as mines adopt autonomous vehicles, humanoid assistance, and federated learning for collaborative intelligence. Energy constrained sensors also experience uneven battery depletion, creating blind spots in safety coverage and disrupting hazard detection pipelines. This paper presents a vision for a Unified Smart Safety and Security Architecture that integrates multimodal perception, secure federated learning, reinforcement learning, DTN enabled communication, and energy aware sensing into a cohesive safety framework. We introduce five core modules: Miner Finder, Multimodal Situational Awareness, Backdoor Attack Monitor, TrustFed LFD, and IoT driven Equipment Health Monitoring. These modules collectively address miner localization, hazard understanding, federated robustness, and predictive maintenance. Together, they form an end to end framework capable of guiding miners through obstructed pathways, identifying compromised models or sensors, and ensuring mission critical equipment reliability. This work outlines a comprehensive research vision for building a resilient and trustworthy intelligent mining system capable of maintaining operational continuity under adversarial conditions.

cs.CR↗

arXiv2025

Explaining the Unseen: Multimodal Vision-Language Reasoning for Situational Awareness in Underground Mining Disasters

Underground mining disasters produce pervasive darkness, dust, and collapses that obscure vision and make situational awareness difficult for humans and conventional systems. To address this, we propose MDSE, Multimodal Disaster Situation Explainer, a novel vision-language framework that automatically generates detailed textual explanations of post-disaster underground scenes. MDSE has three-fold innovations: (i) Context-Aware Cross-Attention for robust alignment of visual and textual features even under severe degradation; (ii) Segmentation-aware dual pathway visual encoding that fuses global and region-specific embeddings; and (iii) Resource-Efficient Transformer-Based Language Model for expressive caption generation with minimal compute cost. To support this task, we present the Underground Mine Disaster (UMD) dataset--the first image-caption corpus of real underground disaster scenes--enabling rigorous training and evaluation. Extensive experiments on UMD and related benchmarks show that MDSE substantially outperforms state-of-the-art captioning models, producing more accurate and contextually relevant descriptions that capture crucial details in obscured environments, improving situational awareness for underground emergency response. The code is at https://github.com/mizanJewel/Multimodal-Disaster-Situation-Explainer.

cs.CV↗

arXiv2024

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

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.

cs.CV↗