Enterprise AI Analysis
Enhanced Escalator Safety with AI-Powered Real-Time Detection and LLM Decision Support
This paper introduces SAL-YOLO-DeepSeek, an innovative framework combining a lightweight real-time object detection model (SAL-YOLO) with a large language model (DeepSeek LLM) for intelligent escalator safety monitoring. Designed to overcome challenges like high passenger density, severe object occlusion, and limited computational resources, SAL-YOLO-DeepSeek delivers high precision, reduced computational complexity, and real-time inference. The system integrates behavior recognition, risk assessment, and dynamic response plan generation, significantly improving proactive safety measures in public infrastructure.
Executive Impact
SAL-YOLO-DeepSeek's innovative design translates directly into tangible benefits for public safety and operational efficiency.
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Deep Analysis & Enterprise Applications
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Model Architecture
The SAL-YOLO model is optimized from YOLOv8n, featuring a StarNet backbone for efficient feature extraction, an Adaptive Image Feature Integration (AIFI) module for global context, and a C2f_star feature fusion module for local feature enhancement. A Lightweight Shared Convolution (LWSC) detection head optimizes computational load with dynamic stride mechanisms. These innovations collectively lead to significant improvements in detection precision and computational efficiency, reducing GFLOPs by 47.1% and parameters by 50% compared to YOLOv8n.
Performance & Efficiency
Experiments on a custom dataset show SAL-YOLO achieves 92.6% Precision, 95.4% mAP@50, and 79.9% mAP@(0.5:0.95), with an inference speed of 500 FPS. The integration of a knowledge distillation framework further boosts performance to 94.6% P and 80.6% mAP@(0.5:0.95). The system is robust in complex scenarios with varying illumination and dense occlusions, demonstrating a favorable trade-off between lightweight design and detection precision suitable for edge deployment.
LLM Integration
The DeepSeek large language model is integrated to establish an end-to-end mechanism of 'behavior recognition – risk assessment – response plan generation'. When abnormal behaviors like falls or bending over are detected, the system dynamically generates graded response strategies and triggers multi-modal alerts. This provides intelligent decision support, shifting from post-incident response to proactive prevention, and is robust across various viewing angles and challenging lighting conditions.
Overall System Flow
The SAL-YOLO-DeepSeek system operates through a comprehensive four-stage end-to-end process.
Video Input
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SAL-YOLO Detection
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Behavior Classification
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DeepSeek LLM Cloud (Risk Assessment & Response)
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Safety Recommendations & Alerts
Superior Performance Post-Distillation
80.6% mAP@(0.5:0.95) with KDAfter applying a knowledge distillation framework with a Similarity-Preserving (SP) feature distillation strategy, SAL-YOLO's performance further improved, achieving a significant boost in precision and overall detection capability.
Model Performance Comparison
SAL-YOLO achieves a superior balance of precision and efficiency compared to state-of-the-art models.
| Model | P | mAP@50 | mAP@(0.5:0.95) | GFLOPs | Params(M) | FPS |
|---|---|---|---|---|---|---|
| SAL-YOLO | 0.926 | 0.954 | 0.799 | 4.6 | 1.6 | 476.2 |
| YOLOv8n | 0.927 | 0.954 | 0.794 | 8.7 | 3.2 | 555.6 |
| NanoDet-Plus-m | 0.901 | 0.935 | 0.759 | 0.9 | 1.2 | 954.7 |
| SAL-YOLO+KD | 0.946 | 0.961 | 0.806 | 4.6 | 1.6 | 500 |
Real-time Fall Detection and Alert
In a real-world scenario, the SAL-YOLO-DeepSeek system successfully detected a passenger fall on an escalator with 93.9% confidence. The system immediately triggered a voice alert and generated a detailed emergency response plan. This plan included halting the escalator, alerting security and medical personnel, providing first aid, clearing the area, and documenting the incident. This rapid, intelligent response significantly reduces potential injury severity and improves operational safety. The DeepSeek LLM provided contextual advice for safe intervention and post-incident management, showcasing the system's ability to act as an expert safety supervisor.
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Your AI Implementation Roadmap
A clear, phased approach to integrating SAL-YOLO-DeepSeek into your operations, ensuring a smooth transition and maximum impact.
Phase 1: Discovery & Strategy
Duration: 2-4 Weeks
Initial consultations, needs assessment, data audit, and strategic planning for AI integration.
Phase 2: Model Customization & Training
Duration: 4-8 Weeks
Customizing SAL-YOLO for specific site conditions and training with enterprise data. DeepSeek prompt engineering.
Phase 3: Deployment & Integration
Duration: 2-4 Weeks
Seamless integration into existing surveillance infrastructure and edge devices.
Phase 4: Monitoring & Optimization
Duration: Ongoing
Continuous performance monitoring, fine-tuning, and scaling of the AI system.
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