Enterprise AI Analysis
Interpretable and lightweight fall detection in a heritage gallery using YOLOv11-SEFA for edge deployment
Falls pose a critical safety risk in aging societies, especially in public venues like heritage galleries. This study introduces YOLOv11-SEFA, an interpretable and lightweight fall detection system designed for challenging environments. Integrating P2 feature enhancement and SimAM attention, it achieves high detection reliability and low computational cost. A four-layer sensing-to-cloud pipeline, coupled with random forest classification of six-dimensional structural features, predicts multi-level fall risk. Feature importance analysis confirms key predictors like aspect ratio, distance to camera, and crowd presence. Practical tests show sub-270 ms latency, low power consumption, and seamless integration, demonstrating operational feasibility in real-world heritage settings for smart city health and safety applications.
Quantifiable Enterprise Impact
The YOLOv11-SEFA model delivers significant improvements in safety monitoring, operational efficiency, and interpretability for heritage and public spaces. Our analysis highlights the core metrics that drive value for enterprise adoption.
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Peak F1 Score
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Latency (Sparse)
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False Alarm Rate (Peak)
Deep Analysis & Enterprise Applications
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Integrated System Architecture for Fall Detection
Perception Layer (Video Acquisition)
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Edge Intelligence Layer (YOLOv11-SEFA)
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Transmission Layer (MQTT/TLS)
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Cloud Service Layer (Alert Fusion/Scheduling)
Computational Efficiency for Edge Deployment
0 Low Computational CostThe YOLOv11-SEFA model maintains a low computational footprint, enabling real-time processing on resource-constrained edge devices like Jetson AGX Orin with only 6.6 GFLOPs. This is crucial for maintaining real-time responsiveness and managing power consumption in heritage gallery environments.
| Model | F1 Score (%) | GFLOPs | Params (MB) | Key Advantages |
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| YOLOv5n | 78.89 | 4.1 | 1.76 |
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| YOLOv8n | 82.89 | 8.1 | 3 |
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| YOLOv11n (Baseline) | 83.04 | 6.3 | 2.58 |
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| YOLOv11-SEFA (Proposed) | 83.99 | 6.6 | 2.67 |
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SHAP Analysis: Key Fall Risk Predictors
SHAP (SHapley Additive exPlanations) analysis confirms the Random Forest model accurately captures expert-defined risk logic. For High Risk (Level 3), Tilt Angle and Pose Area Ratio show strongest positive contributions, consistent with severe falls. BBox Aspect Ratio also strongly indicates 'flattened bounding boxes'. For Safety (Level 0), low Tilt Angle is a primary inhibitor of false alarms. Contextual features like Crowd Presence and Scene Complexity help distinguish intermediate risk states.
Conclusion: The SHAP analysis provides transparent insights, verifying that the YOLOv11-SEFA-RF model robustly learns and applies the hierarchical importance of risk factors, making it reliable for safety-critical deployments.
Multi-Level Fall Risk Prediction
0 Graded Risk Outputs (0-3)Beyond binary fall detection, the system classifies events into four safety levels (0: Safety, 1: Low Risk, 2: Medium-High Risk, 3: High Risk). This granular approach, based on six semantic features and Random Forest classification, enables differentiated response strategies, crucial for tailored safety interventions in public spaces.
Real-time Latency in Crowded Scenes
0 End-to-End LatencyEven under crowded conditions (>5 persons per frame), the system maintained an average end-to-end latency of 312 ms (±28 ms). In sparse scenes, it was 265 ms (±15 ms). This performance is well within acceptable response-time expectations for assisted safety monitoring systems, ensuring timely alerts.
| Challenge | YOLOv11n Baseline Performance | YOLOv11-SEFA Enhancement |
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| Low Light/Noise | False activations on background regions due to texture similarity to human silhouettes. |
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| Reflective Surfaces/Clutter | Grad-CAM activations spatially dispersed, influenced by reflective surfaces. |
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| Partial Occlusion/Crowd | Fails to detect fallen individuals when significant occlusion is present. |
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Pilot Validation at Rochfort Gallery
A 72-hour continuous pilot deployment at Rochfort Gallery (a restored 1920s heritage building) demonstrated operational feasibility. The system achieved a False Alarm Rate of 0.4 alarms/hour during peak times (mostly from squatting/kneeling for photos, classified as low-risk) and 0.05 alarms/hour at night. Zero misses were recorded in 20 staged mock falls (preliminary verification).
Conclusion: This pilot confirms the system's ability to operate under real-world, heritage-sensitive conditions, integrating intelligent safety monitoring without compromising privacy or architectural integrity, paving the way for smart city applications.
Advanced ROI Calculator
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Estimated Annual Cost Savings
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Annual Manual Monitoring Hours Reclaimed
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Implementation Roadmap
Our proven methodology ensures a seamless transition and maximum value realization for your enterprise AI initiatives.
Phase 1: Needs Assessment & Customization (Weeks 1-3)
Identify specific gallery layouts, lighting conditions, and existing infrastructure. Customize YOLOv11-SEFA model parameters and dataset augmentation strategies to specific site requirements. Conduct initial privacy impact assessments.
Phase 2: Edge Hardware Deployment & Network Integration (Weeks 4-7)
Install low-power edge servers (e.g., Jetson AGX Orin) and PoE cameras. Configure RTSP streams and establish TLS-encrypted MQTT channels for secure data transmission. Verify network bandwidth and latency performance.
Phase 3: Model Fine-tuning & Local Validation (Weeks 8-10)
Deploy the YOLOv11-SEFA model on edge devices. Conduct on-site calibration using staged scenarios and real-world data subsets to refine detection thresholds and risk classification. Generate Grad-CAM heatmaps for interpretability checks.
Phase 4: Multi-Level Alert System & Cloud Integration (Weeks 11-14)
Integrate multi-level fall risk predictions (0-3) with cloud-based alert deduplication and scheduling. Develop custom dashboards for real-time monitoring and historical event analysis. Train personnel on system operation and emergency response protocols.
Phase 5: Long-term Monitoring & Iterative Improvement (Ongoing)
Implement continuous performance monitoring, including false alarm rates and latency under varying crowd loads. Gather feedback for iterative model updates and architectural refinements. Explore integration with multimodal sensors and privacy-preserving techniques.
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