Real-Time Detection and Alert System for Construction Worker Safety Gear Using YOLOv5
DOI:
https://doi.org/10.52783/jns.v14.3054Keywords:
Object Detection, YOLOv5 (You Only Look Once), Computer Vision, Deep Learning, Safety Gear ClassificationAbstract
An inventive way to improve safety procedures on building sites is presented by the "Real-time Construction Worker Safety Gear Detection and Alert System using YOLO5" initiative. The technology uses real-time camera feeds and the cutting-edge YOLO5 object identification algorithm to identify and categorise critical safety equipment worn by construction workers. This involves identifying tools that are essential for reducing occupational risks, such as masks, vests, and helmets. Through ongoing video stream analysis, the technology makes sure that employees follow safety protocols by instantly notifying managers of any inconsistencies or lack of safety gear. The system's strong safety product detection module is its fundamental component. It uses YOLO5's sophisticated object detection capabilities to precisely identify safety equipment in the face of shifting worker motions and ambient circumstances. The model gains a high level of precision in identifying safety equipment through rigorous training on annotated datasets, allowing for dependable identification even in dynamic building site situations. Additionally, a built-in email alert mechanism guarantees supervisors are notified right away of any safety gear violations, allowing for prompt action to address the issue and maintain safety regulations. All things considered, the YOLO5 project's Real-time Construction Worker Safety Gear Detection and Alert System offers a complete answer to safety issues in the building sector.
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