MonitorVLM:A Vision Language Framework for Safety Violation Detection in Mining Operations

Abstract--Industrial accidents, particularly in high-risk domains such as surface and underground mining, are frequently caused by unsafe worker behaviors. Traditional manual inspection remains labor-intensive, error-prone, and insufficient for large-scale, dynamic environments, highlighting the urgent need for intelligent and automated safety monitoring. In this paper, we present MonitorVLM, a novel vision-language framework designed to detect safety violations directly from surveillance video streams. MonitorVLM introduces three key innovations: (1) a domain-specific violation dataset comprising 9,000 vision-question-answer (VQA) samples across 40 high-frequency mining regulations, enriched with augmentation and auxiliary detection cues; (2) a clause filter (CF) module that dynamically selects the T op-K most relevant clauses, reducing inference latency by 13.56% while maintaining accuracy; and (3) a behavior magnifier (BM) module that enhances worker regions to improve fine-grained action recognition, yielding additional gains of 3.45% in precision and 8.62% in recall. Experimental results demonstrate that MonitorVLM significantly outperforms baseline vision-language models, achieving improvements of 22.01% in precision, 34.22% in recall, and 28.37% in F1 score over the 72B unfine-tuned baseline. This study highlights the potential of multimodal large models to enhance occupational safety monitoring in mining and beyond. The vast majority of industrial accidents, including those occurring in mining and construction, originate from unsafe worker behaviors, which highlights the urgent need for continuous monitoring and timely early-warning systems [1].