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Related Concept Videos

Survey Safety01:28

Survey Safety

368
Surveying near highways, rough terrain, or power lines involves significant risks. Working along highways is particularly dangerous and requires the use of warning signs and flagmen. It is safest to avoid working directly on roads and use offsets whenever possible. When highway work is unavoidable, it must follow all safety guidelines. Surveyors should wear bright clothing, such as orange reflective vests, to ensure visibility to motorists, coworkers, and hunters. In construction zones, wearing...
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Optimizing Helmet Use Detection in Construction Sites via Fuzzy Logic-Based State Tracking.

Xiaoxiong Zhou1, Xuejun Jia1,2, Jian Bai1,2

  • 1College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211816, China.

Sensors (Basel, Switzerland)
|October 29, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an advanced AI system for construction site safety, accurately detecting helmets and tracking workers even in difficult conditions. The innovative approach significantly reduces errors and improves tracking stability for real-time monitoring.

Keywords:
DeepSORTYOLOv5fuzzy logichelmet detection

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Construction Safety

Background:

  • Automated safety monitoring on construction sites is crucial.
  • Challenges include precise helmet detection and multi-object tracking in complex video sequences with occlusions.

Purpose of the Study:

  • To develop a robust two-stage framework for helmet-status detection and multi-object tracking.
  • To enhance accuracy and reduce identity fragmentation in challenging construction site videos.

Main Methods:

  • A YOLOv5 detector enhanced with self-adaptive coordinate attention (SACA) for improved head-region cue emphasis and small-object discrimination.
  • A DeepSORT tracker with fuzzy-logic gating and temporally consistent update rules to stabilize trajectories and minimize identity fragmentation.

Main Results:

  • The SACA-enhanced YOLOv5 achieved a mAP@0.5 of 0.940, outperforming YOLOv8 and YOLOv9.
  • The tracker achieved 90.5% MOTA and 84.2% IDF1 with only five identity switches.
  • The system demonstrated effectiveness in reducing missed detections and identity fragmentation under occlusion, varied illumination, and camera motion.

Conclusions:

  • The proposed framework provides accurate and stable helmet monitoring for construction sites.
  • The system is suitable for real-time deployment in complex and challenging environments.
  • SACA modules and temporal consistency rules are key to the system's performance improvements.