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Safety Distance Identification for Crane Drivers Based on Mask R-CNN
Zhen Yang1, Yongbo Yuan2, Mingyuan Zhang3
1Department of Construction Management, Dalian University of Technology, Dalian 116024, China. yangz@mail.dlut.edu.cn.
Sensors (Basel, Switzerland)
|June 26, 2019
Summary
This study introduces an automated system using MASK R-CNN and computer vision to detect potential falling object hazards around tower cranes. It enhances construction safety by providing real-time warnings to prevent accidents.
Area of Science:
- Construction Safety Engineering
- Computer Vision Applications
- Artificial Intelligence in Construction
Background:
- Construction sites frequently experience accidents due to falling objects from tower cranes.
- Worker inattention to overhead hazards contributes to struck-by incidents.
Purpose of the Study:
- To develop an automated system for detecting and warning about falling object hazards from tower cranes.
- To enhance safety protocols for tower crane operations and mitigate accident risks.
Main Methods:
- Utilized MASK R-CNN for image recognition on tower crane camera footage.
- Performed RGB color extraction on identified mask layers to pinpoint workers and dangerous zones.
- Implemented a pixel-to-actual distance conversion for safety distance measurement.
Main Results:
- An automatic data collection, analysis, and early-warning system was established.
- The system operates without disrupting normal worker activities.
- Pixel coordinates of workers and dangerous zones were accurately identified.
Conclusions:
- The developed system effectively improves tower crane safety operations.
- Automated inspection and early warning significantly reduce the risk of falling object accidents.
- This technology contributes to a safer construction environment.
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