A Detailed Comparative Analysis of You Only Look Once-Based Architectures for the Detection of Personal Protective Equipment on Construction Sites

Eng Pub Date : 2024-02-21 DOI:10.3390/eng5010019

Abdelrahman Elesawy, Eslam Mohammed Abdelkader, Hesham Osman

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引用次数: 0

Abstract

For practitioners and researchers, construction safety is a major concern. The construction industry is among the world’s most dangerous industries, with a high number of accidents and fatalities. Workers in the construction industry are still exposed to safety risks even after conducting risk assessments. The use of personal protective equipment (PPE) is essential to help reduce the risks to laborers and engineers on construction sites. Developments in the field of computer vision and data analytics, especially using deep learning algorithms, have the potential to address this challenge in construction. This study developed several models to enhance the safety compliance of construction workers with respect to PPE. Through the utilization of convolutional neural networks (CNNs) and the application of transfer learning principles, this study builds upon the foundational YOLO-v5 and YOLO-v8 architectures. The resultant model excels in predicting six key categories: person, vest, and four helmet colors. The developed model is validated using a high-quality CHV benchmark dataset from the literature. The dataset is composed of 1330 images and manages to account for a real construction site background, different gestures, varied angles and distances, and multi-PPE. Consequently, the comparison among the ten models of YOLO-v5 (You Only Look Once) and five models of YOLO-v8 showed that YOLO-v5x6’s running speed in analysis was faster than that of YOLO-v5l; however, YOLO-v8m stands out for its higher precision and accuracy. Furthermore, YOLOv8m has the best mean average precision (mAP), with a score of 92.30%, and the best F1 score, at 0.89. Significantly, the attained mAP reflects a substantial 6.64% advancement over previous related research studies. Accordingly, the proposed research has the capability of reducing and preventing construction accidents that can result in death or serious injury.

查看原文本刊更多论文

基于 "只看一眼 "架构的建筑工地个人防护设备检测详细对比分析

对于从业人员和研究人员来说，建筑安全是一个重大问题。建筑业是世界上最危险的行业之一，事故和死亡人数居高不下。即使进行了风险评估，建筑业工人仍然面临安全风险。使用个人防护设备（PPE）对于帮助降低建筑工地上的工人和工程师的风险至关重要。计算机视觉和数据分析领域的发展，特别是使用深度学习算法，有可能解决建筑业面临的这一挑战。本研究开发了多个模型，以提高建筑工人在个人防护设备方面的安全合规性。通过使用卷积神经网络（CNN）和应用迁移学习原理，本研究建立在基础 YOLO-v5 和 YOLO-v8 架构的基础上。由此产生的模型在预测六个关键类别方面表现出色：人物、背心和四种头盔颜色。我们使用文献中的高质量 CHV 基准数据集对所开发的模型进行了验证。该数据集由 1330 幅图像组成，考虑到了真实的建筑工地背景、不同的手势、不同的角度和距离，以及多装备。因此，在对 YOLO-v5（You Only Look Once）的十个模型和 YOLO-v8 的五个模型进行比较后发现，YOLO-v5x6 的分析运行速度比 YOLO-v5l 快，但 YOLO-v8m 的精度和准确性更高。此外，YOLOv8m 的平均精度（mAP）最高，达到 92.30%，F1 分数最高，为 0.89。值得注意的是，所达到的 mAP 比以前的相关研究大幅提高了 6.64%。因此，拟议的研究有能力减少和预防可能导致死亡或重伤的建筑事故。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Eng

CiteScore

2.10

自引率

0.00%

发文量