Research on application of object detection based on yolov5 in construction site

In the context of the ongoing COVID-19 outbreak, the regulation of the use of personal protective items, specifically helmets and masks, on construction sites is of critical importance. Due to the complex nature of construction environments and the high number of workers, relying on worker inspection and surveillance cameras to detect the wearing of helmets and masks has problems such as poor timeliness, low accuracy, and low efficiency. This paper provides a deep learning approach to address the above issues. We employed YOLOv5s to train separate models for helmet and mask detection, achieving accuracy and mAP@0.5 of greater than 90% for both models. Tested on the divided test set, the mAP@0.5 for both helmet and mask detection were over 96%, demonstrating the effectiveness of our models. These models can be deployed in real environments, effectively solving the problem of monitoring the compliance of construction workers wearing personal protective equipment.