A Deep Learning Approach to Detect Complete Safety Equipment For Construction Workers Based On YOLOv7

TL;DR

This paper presents a deep learning method using YOLOv7 to accurately detect safety equipment on construction workers, aiming to improve safety compliance and reduce accidents.

Contribution

It introduces a novel application of YOLOv7 for safety gear detection with a custom dataset, achieving high accuracy and real-time performance in construction safety monitoring.

Findings

Achieved a mAP@0.5 score of 87.7% in safety equipment detection.

Model demonstrates high precision, recall, and F1-score.

Effective in quickly identifying safety violations on construction sites.

Abstract

In the construction sector, ensuring worker safety is of the utmost significance. In this study, a deep learning-based technique is presented for identifying safety gear worn by construction workers, such as helmets, goggles, jackets, gloves, and footwears. The recommended approach uses the YOLO v7 (You Only Look Once) object detection algorithm to precisely locate these safety items. The dataset utilized in this work consists of labeled images split into training, testing and validation sets. Each image has bounding box labels that indicate where the safety equipment is located within the image. The model is trained to identify and categorize the safety equipment based on the labeled dataset through an iterative training approach. We used custom dataset to train this model. Our trained model performed admirably well, with good precision, recall, and F1-score for safety equipment recognition. Also, the model's evaluation produced encouraging results, with a mAP@0.5 score of 87.7\%. The model performs effectively, making it possible to quickly identify safety equipment violations on building sites. A thorough evaluation of the outcomes reveals the model's advantages and points up potential areas for development. By offering an automatic and trustworthy method for safety equipment detection, this research makes a contribution to the fields of computer vision and workplace safety. The proposed deep learning-based approach will increase safety compliance and reduce the risk of accidents in the construction industry