AI-Powered Construction Safety
AI-Powered Prolonged Violation Detection in Construction: A DGEA-YOLOv8 & ByteTrack Approach
The construction industry, a pillar of economic development, faces significant safety challenges, with human factors like prolonged violation behaviors (e.g., mobile phone use) being a core cause of accidents. These behaviors are subtle, sustained, and hard to detect in real-time. Traditional computer vision methods struggle with the complexity, scale variations, and temporal continuity required for accurate recognition. This analysis explores a novel AI framework to address these challenges.
Executive Summary: Boosting Construction Safety with AI
This research introduces DGEA-YOLOv8, an enhanced object detection model combined with ByteTrack for multi-object tracking, specifically designed for real-time recognition of prolonged violation behaviors (like mobile phone use) in complex construction environments. Key enhancements include improved adaptability to pose deformation, multi-scale perception, and temporal stability.
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mAP50 Improvement
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ID Switch Rate Reduction
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Real-time Detection Rate
Deep Analysis & Enterprise Applications
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The DGEA-YOLOv8 model significantly enhances the YOLOv8 baseline by integrating DCNv3 for adaptive geometric modeling, GELAN for efficient feature aggregation, ECA for channel attention, and ASPP for multi-scale context. These modules collectively improve robustness against deformation, occlusion, and varying target scales, crucial for subtle violation behaviors.
Architectural Enhancements Impact
The DGEA-YOLOv8 integrates DCNv3, GELAN, ECA, and ASPP to address limitations of the standard YOLOv8s. Each module targets specific challenges in construction environments, from pose variation to small object detection and multi-scale perception. The table below highlights their combined effect on detection performance.
| Feature | Benefit |
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| DCNv3 Integration |
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| GELAN Architecture |
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| ECA Attention Mechanism |
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| ASPP Module |
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Prolonged violation recognition requires stable identity tracking and temporal consistency. ByteTrack is integrated for its efficiency and robustness in multi-object tracking, especially in scenarios with occlusions and subtle movements. It maintains trajectory continuity by leveraging both high- and low-confidence detections.
Overall Technical Scheme Flowchart For Prolonged Violation Behavior Recognition
The integrated recognition framework, DGEA-YOLOv8 algorithm, combines an improved YOLO-based object detection model with a lightweight multi-object tracking algorithm (ByteTrack) and a temporal window-based decision strategy.
Dataset Construction
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Model Improvement (DGEA-YOLOv8)
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Multi-Object Tracking (ByteTrack)
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Behavior Decision (Sliding Window)
ByteTrack's Superior Tracking Performance
2.3% Track Localization Recall (TLR) for ByteTrackByteTrack achieved a TLR of 2.3%, demonstrating significantly better data capture and continuity compared to other algorithms like Deepsort (11.9%) and Strongsort (21.6%), ensuring robust tracking of prolonged behaviors.
The system defines a 'prolonged violation' as a sustained behavior (e.g., phone use for >5 seconds) to filter out transient actions. A sliding temporal window strategy (30 frames, 10-frame stride) calculates detection frequency, average confidence, and continuous detection length across five windows to make robust decisions.
Mitigating False Positives with Multi-Window Analysis
Scenario: In complex construction environments, relying solely on single-frame detections leads to frequent false alarms (e.g., brief phone appearances, reflective objects).
Solution: The DGEA-YOLOv8 framework integrates a cross-frame association mechanism with multi-window temporal statistics. Decision thresholds are set (detection frequency ≥ 60%, mean confidence ≥ 0.45, continuous length > 10 frames).
Outcome: When five consecutive windows satisfy these criteria, a sustained phone-use behavior is confirmed, substantially reducing false-positive risks and distinguishing actual violations from incidental events.
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Annual Cost Savings
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Hours Reclaimed Annually
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Your AI Safety Implementation Roadmap
A structured approach to integrating DGEA-YOLOv8 and ByteTrack into your operations.
Phase 1: Discovery & Customization
Assess site-specific needs, integrate existing camera infrastructure, and fine-tune model for unique environmental conditions and violation types. Data annotation and initial model training.
Phase 2: Pilot Deployment & Validation
Deploy on a subset of cameras, run parallel with existing monitoring, and collect real-world performance data. Refine detection thresholds and tracking parameters based on pilot feedback.
Phase 3: Full-Scale Integration & Training
Expand deployment across all relevant areas, integrate with existing safety management systems, and train personnel on new AI-powered monitoring tools. Establish alert protocols.
Phase 4: Continuous Optimization & Scaling
Monitor performance metrics, apply model updates and retrain with new data for evolving behaviors or environmental changes. Explore expansion to other safety applications.
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