Deep Learning-Based Workers Safety Helmet Wearing Detection on Construction Sites Using Multi-Scale Features
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Abstract:
Due to a lack of knowledge about safety helmets, accidents and injuries on construction sites are now increasingly common. Worker supervision by hand is challenging and ineffective. Workers often take off the helmets because of weak security-conscious and discomfort, then hidden dangers will be brought by this behaviour. Workers without safety helmets will suffer more injuries in accidents such as falling human body and vertical falling matter. Hence, detecting safety helmet wearing is a vital step of construction sites safety management and a safety helmet detector. However, traditional manual monitor is labour intensive and methods of installing sensors on safety helmet are difficult to popularize. This study is visually checking the construction site to see if anyone is wearing a safety helmet. In order to recognise a safety helmet in real time at a building site, we built a deep learning-based technique.
Proposed System:In proposed system, we have used YOLOv5 to detect the object. YOLOv5 is the latest and the lightweight version of previous YOLO algorithms and uses Focus structure with CSPdarknet53 as a backbone. The Focus layer is first introduced in YOLOv5. The Focus layer replaces the first three layers in the YOLOv3 algorithm.YOLOv5 uses auto learning bounding boxes which improves the overall accuracy of the algorithm that can detect objects more accurately compared to YOLOv3.
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The Delivery time for Hardware Mini projects is 7-8 working days.