Research Paper Analysis
An Adaptive Framework for Violation Detection Using Multimodal Large Models
Authors: Yuhan Ma, Fangye Wang, Feiyan Yin, Xinyue Li, Zhongguo Yang, Liping Zhu
Publication: AIFM '25: Proceedings of the 2025 International Conference on Artificial Intelligence and Foundation Model (November 2025), Guangzhou, China. DOI: 10.1145/3786709.3786713, ISBN: 9798400715051.
This paper introduces a novel zero-shot detection framework integrating multimodal large language models (MLLMs) with Retrieval-Augmented Generation (RAG) for adaptive, interpretable violation detection in industrial safety monitoring.
Executive Impact & Key Findings
The proposed framework addresses critical limitations in traditional video-based safety monitoring, offering significant improvements in adaptability, accuracy, and cost-efficiency for industrial applications.
Overall F1-Score (Action-Tiny Dataset)
Highest F1-Score (Cross Fence Detection)
Precision Boost vs. MLLM Baseline
Training Required for New Rules
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Overview of the Adaptive Violation Detection Framework
This research addresses the limitations of traditional video-based safety monitoring, which struggle with context-dependent rules and high annotation costs. The proposed framework leverages Multimodal Large Language Models (MLLMs) combined with Retrieval-Augmented Generation (RAG) to enable zero-shot, adaptive violation detection. It achieves this by dynamically identifying operational contexts, matching them with relevant regulations, and providing interpretable judgments. This innovative approach promises more flexible and efficient intelligent safety monitoring for diverse industrial settings.
Framework for Adaptive Violation Detection
The core innovation lies in a three-tier analysis process that integrates visual analysis, natural language processing, and dynamic reasoning.
Enterprise Process Flow
1. Spatiotemporal Feature Extraction & Dynamic Domain Identification
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2. Safety Regulation Retrieval
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3. Action Segmentation & Interpretable Decision-Making
This process begins with the MLLM generating structured scene descriptions from video content. These descriptions are then used to automatically select the most relevant domain-specific knowledge base. A natural language model matches the scene description with pre-established safety regulations. Finally, the system combines retrieved regulations with segmented actions to form context-aware queries, leading to interpretable violation judgments.
Performance Benchmarking and Ablation Studies
The framework demonstrates superior performance across various tasks compared to MLLM Zero-shot Baselines, particularly in precision and overall F1-score, validating the effectiveness of knowledge enhancement and structured reasoning.
85.1%
Accuracy of Our Full Model (Action-Tiny Dataset)
| Method | Average Accuracy | Average F1-score |
|---|---|---|
| MLLM Zero-shot Baseline | 49.2% | 53.8% |
| Our Proposed Method | 86.5% | 83.1% |
Ablation studies confirm the individual contributions: Dynamic Domain Identification significantly improves precision from 55.9% to 69.7%, Automated Knowledge Base refines all metrics, and Two-Stage Prompting boosts recall to 86.6%, cementing overall performance.
Enterprise Scalability & Adaptive Rule Management
A significant advantage of this framework is its ability to adapt to new rules and environments without retraining, offering unparalleled scalability for diverse industrial settings.
Real-World Adaptability: Dynamic Safety Monitoring
Challenge: Traditional systems for industrial safety monitoring require constant retraining with new labeled data when safety regulations change or equipment iterates, leading to high costs and poor generalization across different factory floors or construction sites.
Our Solution: The Adaptive Framework for Violation Detection automatically identifies operational contexts and dynamically matches them with corresponding regulations. Through its automated knowledge base construction and zero-shot detection capabilities, the system seamlessly integrates the latest safety rules without requiring model retraining.
Impact: This enables a single system to function effectively across various industrial environments (e.g., factories, power plants, construction sites) with minimal maintenance. Rule updates are simple, and adaptation to new scenarios is rapid, ensuring continuous compliance and significantly reducing operational costs and human intervention.
This innovative paradigm ensures that even weekly changes in regulations can be swiftly incorporated, making the system robust against the rapid obsolescence faced by static rule sets.
Calculate Your Potential AI Impact
Estimate the potential time and cost savings for your organization by implementing an adaptive AI solution for safety monitoring.
Estimated Annual Savings
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Annual Hours Reclaimed
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Your AI Implementation Roadmap
A typical timeline for integrating advanced AI solutions for adaptive safety monitoring into your enterprise operations.
Phase 1: Discovery & Strategy (2-4 Weeks)
Initial consultations to understand your specific safety monitoring needs, existing infrastructure, and regulatory environment. Define project scope, KPIs, and custom knowledge base requirements.
Phase 2: Data Integration & Knowledge Base Setup (4-8 Weeks)
Integrate video feeds and establish secure data pipelines. Develop and populate the automated knowledge base with your specific safety regulations, policies, and operational contexts.
Phase 3: Customization & Model Deployment (6-10 Weeks)
Tailor the Multimodal Large Model and RAG components to your domain. Initial deployment in a pilot environment, fine-tuning of scene parsing and rule matching. Begin user training.
Phase 4: Optimization & Scalability (Ongoing)
Continuous monitoring and performance optimization. Expand deployment across additional sites or scenarios. Regular updates to the knowledge base and model for evolving regulations and equipment.
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