A multi-object detection method for building fire warnings through artificial intelligence generated content
AI-Powered Fire Detection for Enhanced Building Safety
This paper presents a novel multi-object detection method leveraging Artificial Intelligence Generated Content (AIGC) to enhance building fire warning systems. By overcoming data scarcity issues through synthetic image generation, and incorporating advanced neural network mechanisms (MLCA and VOV-GSCSP) into a YOLOv8-based model, the proposed system achieves high accuracy (95.7%) and significantly faster detection times (within 2 seconds) compared to traditional alarms (6.5 to 10 times faster). This innovation promises to provide critical early warnings, improving evacuation safety and reducing fire-related casualties in smart building environments.
Key Performance Metrics
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Model Detection Accuracy
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AIGC Dataset Performance Gap
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Detection Speed Improvement (Avg)
Deep Analysis & Enterprise Applications
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The research effectively addresses the scarcity of real building fire images by employing AIGC, specifically Midjourney, to generate diverse synthetic datasets. This approach ensures robust model training, with the AIGC-trained model achieving a detection accuracy only 1.6% lower than a model trained on a real image dataset, validating its utility and reliability for complex fire scenarios. This represents a significant leap forward in overcoming data limitations for critical safety applications.
A novel YOLOv8-based multi-object detection model is developed, integrating the Mixed Local Channel Attention (MLCA) mechanism into its backbone and replacing the feature fusion layer in its neck with VOV-GSCSP. This architecture enhances feature capture and fusion capabilities, allowing the model to detect flame and smoke with 95.7% accuracy. Ablation studies confirm the effectiveness of these modifications, demonstrating improved precision and mAP@0.5 over the baseline YOLOv8 model.
The proposed method demonstrates superior real-time performance through case studies of actual fire incidents. It can detect fires within 2 seconds of ignition, an improvement of at least 6.5 to 10 times faster than traditional fire alarms. This rapid detection capability, utilizing indoor security cameras, is crucial for timely evacuation and activation of suppression systems, significantly reducing the risk of fire-related casualties and damage in buildings.
95.7%
Achieved Detection Accuracy (Flame & Smoke)
Enterprise Process Flow
Define Image Range & Scenarios
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Extract Fire-related Keywords
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Generate Images with Midjourney
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AIGC Dataset Validation
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Multi-Object Model Training
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Real-time Fire Detection
| Case | Proposed Method Detection Time | Traditional Alarm Detection Time | Efficiency Improvement (x) |
|---|---|---|---|
| Case 1 (Complex Environment) | 2 seconds | 20 seconds | 10x |
| Case 2 (Small Target, Fast Spread) | 2 seconds | 13 seconds | 6.5x |
| Case 3 (Low Clarity, Transient) | 1 second | 7 seconds | 7x |
Timely Fire Warning in Complex Environments
Case studies demonstrate the model's ability to detect fires rapidly in challenging real-world scenarios. In a warehouse fire with multiple obstacles, the model detected flame within 2 seconds, compared to 20 seconds for traditional alarms. Another case involving a fast-spreading fireworks ignition was detected within 2 seconds, showing its robustness against small, rapidly evolving targets. These results underscore the critical advantage of AI-driven video surveillance in providing immediate fire warnings, far surpassing the response times of conventional sensor-based systems. This directly translates to increased evacuation time and reduced potential for casualties.
- Rapid Detection: Consistent detection within 2 seconds across diverse cases.
- Complex Scene Robustness: Effective in environments with obstacles and low clarity.
- Significant Efficiency: Up to 10x faster warning than traditional systems, saving critical time for response.
Advanced ROI Calculator
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AI Implementation Roadmap
A structured approach to integrating cutting-edge AI for superior fire detection.
Phase 1: AI Readiness Assessment & Data Strategy
Evaluate existing surveillance infrastructure, identify data integration points, and define AIGC data generation parameters for scenario-specific training. Establish initial performance benchmarks and security protocols.
Phase 2: Model Customization & AIGC Integration
Customize the multi-object detection model (MLCA-YOLOv8) for your specific building types and fire risks. Integrate AIGC workflow to generate synthetic training data, enhancing model robustness for rare or complex fire scenarios. Begin initial model training on combined real and synthetic datasets.
Phase 3: System Deployment & Continuous Optimization
Deploy the AI system with indoor security cameras for real-time monitoring. Conduct rigorous testing and validation in simulated environments. Implement continuous learning loops for model refinement based on new data and operational feedback, ensuring long-term accuracy and efficiency.
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