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

arXiv: https://arxiv.org/abs/2510.03666 · HTML: https://mlsanity.com/paper/2510.03666/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 33 references that are in the map.
- First posted on arXiv 4 Oct 2025.

## Builds on (most cited first, 16 of 16)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data](https://mlsanity.com/paper/2107.10833/) (2021)
- [Vision-Language Models for Vision Tasks: A Survey](https://mlsanity.com/paper/2304.00685/) (2023)
- [SWIFT:A Scalable lightWeight Infrastructure for Fine-Tuning](https://mlsanity.com/paper/2408.05517/) (2024)
- [BERT: A Review of Applications in Natural Language Processing and Understanding](https://mlsanity.com/paper/2103.11943/) (2021)
- [DriveMoE: Mixture-of-Experts for Vision-Language-Action Model in End-to-End Autonomous Driving](https://mlsanity.com/paper/2505.16278/) (2025)
- [GPT-ImgEval: A Comprehensive Benchmark for Diagnosing GPT4o in Image Generation](https://mlsanity.com/paper/2504.02782/) (2025)
- [LLMDet: Learning Strong Open-Vocabulary Object Detectors under the Supervision of Large Language Models](https://mlsanity.com/paper/2501.18954/) (2025)
- [Object Detection with Multimodal Large Vision-Language Models: An In-depth Review](https://mlsanity.com/paper/2508.19294/) (2025)
- [Using Vision Language Models for Safety Hazard Identification in Construction](https://mlsanity.com/paper/2504.09083/) (2025)
- [Vision Language Model for Interpretable and Fine-grained Detection of Safety Compliance in Diverse Workplaces](https://mlsanity.com/paper/2408.07146/) (2024)
- [Prompt to Protection: A Comparative Study of Multimodal LLMs in Construction Hazard Recognition](https://mlsanity.com/paper/2506.07436/) (2025)

## Built on by (most cited first, 4 of 4)

- [CodecSight: Leveraging Video Codec Signals for Efficient Streaming VLM Inference](https://mlsanity.com/paper/2604.06036/) (2026)
- [TSHA: A Benchmark for Visual Language Models in Trustworthy Safety Hazard Assessment Scenarios](https://mlsanity.com/paper/2603.29759/) (2026)
- [Multi-modal Rail Crossing Safety Analysis](https://mlsanity.com/paper/2607.01365/) (2026)
- [MonitorVLM-v2: A Deployed Vision-Language Framework for Real-Time Safety Violation Detection](https://mlsanity.com/paper/2608.00975/) (2026)

## Abstract (arXiv)

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 Top-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. A lightweight web-based interface further integrates MonitorVLM into practical workflows, enabling automatic violation reporting with video timestamping. This study highlights the potential of multimodal large models to enhance occupational safety monitoring in mining and beyond.