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Fast Personal Protective Equipment Detection for Real Construction Sites Using Deep Learning Approaches

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School of Civil Engineering, Central South University, Changsha 410075, China
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School of Aerospace, Transport and Manufacturing, Cranfield University, Bedfordshire MK43 0AL, UK
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BAM Nuttall, St James House, Knoll Road, Camberley GU15 3XW, UK
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Author to whom correspondence should be addressed.
Academic Editor: Petros Daras
Sensors 2021, 21(10), 3478; https://doi.org/10.3390/s21103478
Received: 16 April 2021 / Revised: 10 May 2021 / Accepted: 11 May 2021 / Published: 17 May 2021
(This article belongs to the Section Physical Sensors)
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. View Full-Text
Keywords: PPE; construction safety; deep learning; You Only Look Once (YOLO); image dataset; real-time detection PPE; construction safety; deep learning; You Only Look Once (YOLO); image dataset; real-time detection
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MDPI and ACS Style

Wang, Z.; Wu, Y.; Yang, L.; Thirunavukarasu, A.; Evison, C.; Zhao, Y. Fast Personal Protective Equipment Detection for Real Construction Sites Using Deep Learning Approaches. Sensors 2021, 21, 3478. https://doi.org/10.3390/s21103478

AMA Style

Wang Z, Wu Y, Yang L, Thirunavukarasu A, Evison C, Zhao Y. Fast Personal Protective Equipment Detection for Real Construction Sites Using Deep Learning Approaches. Sensors. 2021; 21(10):3478. https://doi.org/10.3390/s21103478

Chicago/Turabian Style

Wang, Zijian, Yimin Wu, Lichao Yang, Arjun Thirunavukarasu, Colin Evison, and Yifan Zhao. 2021. "Fast Personal Protective Equipment Detection for Real Construction Sites Using Deep Learning Approaches" Sensors 21, no. 10: 3478. https://doi.org/10.3390/s21103478

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