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© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

The existing deep learning-based Personal Protective Equipment (PPE) detectors can only detect limited types of PPE and their performance needs to be improved, particularly for their deployment on real construction sites. This paper introduces an approach to train and evaluate eight deep learning detectors, for real application purposes, based on You Only Look Once (YOLO) architectures for six classes, including helmets with four colours, person, and vest. Meanwhile, a dedicated high-quality dataset, CHV, consisting of 1330 images, is constructed by considering real construction site background, different gestures, varied angles and distances, and multi PPE classes. The comparison result among the eight models shows that YOLO v5x has the best mAP (86.55%), and YOLO v5s has the fastest speed (52 FPS) on GPU. The detection accuracy of helmet classes on blurred faces decreases by 7%, while there is no effect on other person and vest classes. And the proposed detectors trained on the CHV dataset have a superior performance compared to other deep learning approaches on the same datasets. The novel multiclass CHV dataset is open for public use.

Details

Title
Fast Personal Protective Equipment Detection for Real Construction Sites Using Deep Learning Approaches
Author
Wang, Zijian 1   VIAFID ORCID Logo  ; Wu, Yimin 2 ; Yang, Lichao 3   VIAFID ORCID Logo  ; Thirunavukarasu, Arjun 4 ; Evison, Colin 4   VIAFID ORCID Logo  ; Zhao, Yifan 3   VIAFID ORCID Logo 

 School of Civil Engineering, Central South University, Changsha 410075, China; [email protected] (Z.W.); [email protected] (Y.W.); School of Aerospace, Transport and Manufacturing, Cranfield University, Bedfordshire MK43 0AL, UK; [email protected] 
 School of Civil Engineering, Central South University, Changsha 410075, China; [email protected] (Z.W.); [email protected] (Y.W.) 
 School of Aerospace, Transport and Manufacturing, Cranfield University, Bedfordshire MK43 0AL, UK; [email protected] 
 BAM Nuttall, St James House, Knoll Road, Camberley GU15 3XW, UK; [email protected] (A.T.); [email protected] (C.E.) 
First page
3478
Publication year
2021
Publication date
2021
Publisher
MDPI AG
e-ISSN
14248220
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2532959877
Copyright
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.