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YOLOv4 algorithm for the real-time detection of fire and personal protective equipments at construction sites

  • 1200: Machine Vision Theory and Applications for Cyber Physical Systems
  • Published:
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Abstract

Many difficulties are encountered during evacuation from construction sites in hazardous situations, which may lead to severe fatalities. These fatalities, especially caused by fire, may be significantly reduced by ensuring personal protective equipment (PPE) compliance of construction site workers and fire detection through proper surveillance. Thus, the detection of PPEs, fire and injured or trapped persons, can greatly assist in the reduction of fatalities and economic loss. This article presents a novel approach towards the detection of fire and PPEs to assist in the monitoring and evacuation tasks. This work utilizes the YOLOv4 and YOLOv4-tiny algorithms based on deep learning for carrying out the detection task. A self-made dataset has been utilized to train the model in the Darknet neural network framework. Moreover, a comparative analysis with previous works has been carried out in order to endorse the real-time efficacy of the proposed work. The results verify the strength of YOLOv4 algorithm in real-time detection and surveillance at construction sites with maximum mean average precision (mAP) of 76.86 %.

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Abbreviations

AP:

Average Precision

CNN:

Convolutional Neural Networks

DL:

Deep Learning

IoU:

Intersection over Union

mAP:

Mean Average Precision

PPE:

Personal Protective Equipment

RFID:

Radio Frequency Identification

YOLO:

You Only Look Once

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Authors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology, Roorkee, India

    Saurav Kumar

  2. Department of Instrumentation and Control Engineering, Dr. B. R. Ambedkar National Institute of Technology Jalandhar, Jalandhar, India

    Himanshu Gupta & Om Prakash Verma

  3. Faculty of Informatics, Technische Universität Wien, 1040, Vienna, Austria

    Drishti Yadav

  4. Department of Electronics and Communication Engineering, Design and Manufacturing, PDPM Indian Institute of Information Technology, Jabalpur, India

    Irshad Ahmad Ansari

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  1. Saurav Kumar
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  2. Himanshu Gupta
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  3. Drishti Yadav
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  4. Irshad Ahmad Ansari
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  5. Om Prakash Verma
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Correspondence to Om Prakash Verma.

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Kumar, S., Gupta, H., Yadav, D. et al. YOLOv4 algorithm for the real-time detection of fire and personal protective equipments at construction sites. Multimed Tools Appl 81, 22163–22183 (2022). https://doi.org/10.1007/s11042-021-11280-6

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