A Comprehensive Review Of Ai-based Construction Site Safety Monitoring Using Computer Vision And Deep Learning

Abstract

Construction Site Safety Is A Serious Concern Due To The Complex And Risky Nature Of Construction Activities. Traditional Safety Monitoring Methods, Such As Manual Supervision And CCTV Systems, Are Commonly Used But Have Several Limitations, Including Dependence On Human Observation, Inconsistency, And Lack Of Real-time Response. With Recent Advancements In Computer Vision And Deep Learning, Automated Safety Monitoring Systems Have Started Gaining Attention. These Systems Can Detect Safety Violations, Such As Absence Of Personal Protective Equipment (PPE) Or Unsafe Worker Behavior, Directly From Images And Video Data In Real Time. This Paper Presents A Review Of Existing Research Related To Construction Safety Monitoring. It Covers Traditional Safety Practices, Computer Vision-based Approaches, And Deep Learning Models Such As YOLO[4], R-CNN[9], And Transformer-based Methods[10]. Different Studies Are Analyzed And Compared Based On Their Methodology, Performance, And Practical Use In Real-world Conditions. The Review Also Identifies Key Research Gaps, Including Limited Real-world Implementation, Lack Of Integration With Construction Management Systems, And Challenges Caused By Environmental Conditions Like Lighting And Occlusion. Finally, The Paper Discusses Possible Future Directions, Such As The Use Of Hybrid Systems, Integration With IoT Devices, And Development Of More Efficient Real-time Monitoring Solutions

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