Railway Power Supply Operation Video AI Evaluation Platform: Application of Multimodal Large Models in Industrial Safety

Core Information

• Project Name: Railway Power Supply Operation Video AI Evaluation Platform
• Core Technologies: Integration of computer vision, action recognition, multimodal large models, and rule-based scoring
• Goal: Achieve automated safety assessment of railway power supply operation processes
• Source: XuelinHu Open Source Project (GitHub link: https://github.com/XuelinHu/railway-power-operation-video-ai-evaluator)
• Release Time: June 3, 2026

This platform addresses the problems of low efficiency and inconsistent standards in traditional manual assessment. By building an intelligent assessment system using multimodal technologies, it provides a solution for the digital transformation of industrial safety.

Railway power supply operations have three key characteristics that make them suitable for AI applications:

1. Standardized Processes: Operations follow strict procedures with clear step sequences and safety requirements, facilitating rule modeling.
2. High Risk: Operational errors with high-voltage equipment can easily lead to serious accidents, and manual audits struggle to ensure consistency.
3. Complete Video Records: The operation site is equipped with comprehensive monitoring devices, providing a rich data foundation.

Computer Vision Layer

• Region Recognition: Semantic segmentation to identify equipment areas (transformers, circuit breakers, etc.), safety areas (insulation mats, fences), and personnel areas.
• Object Detection: Locate personnel, identify tools (insulation rods, electroscopes), detect equipment status (switch on/off, indicator lights), and verify protective gear (safety helmets, insulation gloves).

Action Recognition Layer

• Temporal Modeling: Use 3D convolution/Transformer to analyze continuous frames, classify operational actions (electrical testing, grounding wire installation, etc.), locate action start/end times, and verify process completeness.
• Pose Estimation Assistance: Check safety postures (distance, standing position) and operational norms (amplitude, force).

Multimodal Large Model Layer

• Visual Question Answering: Understand natural language queries (e.g., "Was electrical testing performed before operation?") and output judgments along with supporting evidence.
• Anomaly Description Generation: Automatically generate natural language explanations when violations occur (e.g., "Grounding wire was installed without prior electrical testing, violating Regulation X").
• Context Reasoning: Adjust assessment standards based on scenarios (e.g., rainy days) to distinguish between normal operations and emergency responses.

Rule-Based Scoring Layer

• Rule Configuration: Support rules such as basic mandatory steps, sequence dependencies, time ranges, and spatial constraints.
• Scoring Algorithms: Deduction system (based on severity), weighted scoring (higher weights for key steps), and trend analysis (comparison with historical operations).

Video Preprocessing

• Format conversion (supports multiple monitoring formats), quality enhancement (low-light compensation, jitter correction), and segment processing (split long videos into operation units).

Inference Optimization

• Model quantization (adapt to edge devices), batch processing (parallel processing of multiple videos), and caching mechanism (reuse results for similar scenarios).

Result Presentation

• Timeline annotation of key events, heatmaps showing personnel activity areas, and structured assessment reports (scores + violation details).

1. Efficiency Improvement: Manual review of a 30-minute video takes 30-60 minutes; AI completes initial screening in minutes, and manual work only needs to review marked segments.
2. Standard Unification: Eliminate subjective differences among auditors to ensure assessment consistency.
3. Training Improvement: Accumulate violation data to optimize training content in a targeted manner.
4. Risk Early Warning: Identify high-risk operational habits and intervene in advance.

This platform demonstrates the key logic for industrial AI implementation: focus on specific scenarios (railway power supply operations), integrate domain knowledge with multimodal technologies, and build an explainable and configurable system. Such projects provide a replicable technical paradigm for industrial digital transformation, proving that AI does not need to pursue general intelligence—precise applications in vertical fields have more practical value.