Intelligent system for illegal behavior safety supervision based on improved YoLov5 and generative visual big model
Revolutionizing Enterprise Operations with AI: Deep Dive into Illegal Behavior Safety Supervision
This paper presents an intelligent recognition system for safety violations at power grid construction sites. It uses an enhanced YOLOv5 model with spatial and channel attention mechanisms, combined with a generative visual big model to address data scarcity by synthesizing negative samples. The system detects violations like not wearing safety helmets or insulated gloves, improving accuracy and recall. Experimental results show a mAP of 94%, demonstrating strong detection ability and robustness in identifying personnel and safety tools.
Executive Impact: Measurable Results from Advanced AI
Our enhanced AI system delivers tangible improvements in safety, efficiency, and operational oversight, as demonstrated by key performance indicators from real-world applications.
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Detection mAP
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Daily Images Processed (Province-wide)
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Accuracy Improvement from Gen AI
Deep Analysis & Enterprise Applications
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Focuses on the core YOLOv5 enhancement for object detection in safety supervision. This includes the integration of spatial and channel attention mechanisms into the Neck section and a spatial attention mechanism after the SPP of the backbone network. These modifications significantly boost the model's ability to extract and utilize critical spatial and channel features, improving detection accuracy and recall for various safety violations like improper PPE usage.
Details the innovative use of a generative visual big model (specifically Pangu Generative Large Model) to create synthetic negative samples. This addresses the challenge of insufficient negative data in real-world scenarios, which is crucial for robust model training. By learning from both positive and negative labeled examples, the generative model enhances the dataset, allowing the YOLOv5 model to better learn and distinguish between correct and incorrect safety behaviors, leading to a 5% accuracy improvement.
Outlines the architectural design of the real-time safety supervision system. It involves IoT devices (cameras), intermediate data servers for filtering, and AI workstations for detection. The system processes approximately 30 million images daily, triggering immediate alarms and notifications. This section emphasizes the system's ability to operate efficiently in real-time, providing proactive monitoring and analysis of construction site safety, crucial for preventing high-risk incidents.
94.0%
Achieved mAP demonstrates robust detection across scenarios.
Enterprise Process Flow
Real-time Image Capture (IoT Cameras)
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Data Server Filtering (1 image/5s)
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AI Workstation (Improved YOLOv5)
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Violation Detection & Alerting
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Historical Data Analysis & Prevention
| Model Variant | Key Improvement | Performance Boost (mAP90%) |
|---|---|---|
| Original YOLOv5 | Baseline | 88.30% |
| YOLOv5 + Neck SCAM | Spatial Channel Attention in Neck | 89.50% |
| YOLOv5 + Backbone Spatial Attention | Spatial Information Enhancement in Backbone | 89.40% |
| YOLOv5 (Combined & Gen AI) | Full Proposed Model + Generative Samples | 94.0% |
Real-world Application: Power Grid Safety
The implemented system monitors power grid construction sites across a province, processing approximately 30 million images daily. It successfully identifies critical safety violations such as unhelmeted personnel, improper work attire, and unmanned dangerous areas. The real-time alerting mechanism ensures that relevant personnel are notified promptly, enabling proactive intervention and risk mitigation on high-risk sites. This robust detection capability directly translates into enhanced safety protocols and reduced incident rates.
Primary Business Outcome: Reduced Incident Rates
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Your AI Transformation Roadmap: From Concept to Reality
A structured approach ensures seamless integration and maximum impact for your intelligent safety supervision system.
Pilot Program & Data Collection
Deploy initial cameras and improved YOLOv5 to capture a diverse dataset of construction site activities. Begin initial training with real data and use generative models to expand negative samples for enhanced robustness. Establish baseline performance metrics.
Model Refinement & Integration
Iteratively refine the YOLOv5 model with newly generated and real data. Integrate the enhanced model with intermediate data servers and alerting mechanisms. Conduct comprehensive testing in controlled environments.
Full-scale Deployment & Monitoring
Roll out the system across all target power grid construction sites. Activate real-time monitoring and alerting. Establish protocols for incident response and data analysis feedback loops to continuously improve the system.
Continuous Improvement & Expansion
Regularly update the model with new data and adapt to evolving safety regulations. Explore integration with other enterprise systems (e.g., HR, project management) for broader impact. Focus on predictive analytics for even earlier risk identification.
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