Personal Protective Equipment (PPE) detection plays a critical role in ensuring workplace safety and compliance with industrial regulations. Traditional object detection algorithms, such as YOLO (You Only Look Once), provide real-time and accurate detection capabilities but often require extensive manual tuning of hyperparameters and anchor boxes for optimal performance. This paper explores the integration of evolutionary computation with YOLO to develop an adaptive, high-precision PPE detection system. Due to the impossibility of 24-h human supervision, it has long been not easy to guarantee the use of PPE. However, such monitoring may likely be carried out using technological aids or automated programs. The current study outlines a systematic method for tracking employees’ PPEs, like hard hats, safety vests, etc., in real-time using Deep Learning (DL) models constructed on the YOLO architecture. The suggested method employs a small architecture of YOLO (i.e., YOLOv8s) and a Genetic Algorithm (GA) based evolutionary computation for object detection and localization. With this method, we have built a model with a Mean Average Precision (mAP) value of 87.2% on the validation data set and 83.1% on the test data set, highlighting the effectiveness of evolutionary optimization in refining object detection performance. This framework presents a scalable and automated solution for PPE monitoring, contributing to enhanced workplace safety through Artificial Intelligence.
AshrafH.ImranM.QahtaniA.AlsufyaniA.AlmutiryO.MahmoodA.KhanM.HabibM. (2022). Weapons detection for security and video surveillance using CNN and YOLO-V5s. Computers, Materials and Continua, 70(2), 2761–2775. https://doi.org/10.32604/cmc.2022.018785
2.
BadgujarC.PouloseA.GanH. (2024). Agricultural Object Detection with You Look Only Once (YOLO). Algorithm: A Bibliometric and Systematic Literature Review. https://doi.org/10.48550/arXiv.2401.10379
3.
ChenC.ZhenhuaZ.AminH. (2020). Automated excavators activity recognition and productivity analysis from construction site surveillance videos. Automation in Construction, 110, 103045. https://doi.org/10.1016/j.autcon.2019.103045
4.
DebK.RamB. A. (1995). Simulated Binary Crossover for Continuous Search Space. Complex Systems, 9(2), 115–148.
5.
DihuaW.ShuaichaoL.MeiJ.HuaiboS. (2020). Using channel pruning-based YOLO v4 deep learning algorithm for the real-time and accurate detection of apple flowers in natural environments. Computers and Electronics in Agriculture, 178, 105742. https://doi.org/10.1016/j.compag.2020.105742
6.
GoldbergD. E. (1989). Genetic algorithms in search, optimisation, and machine learning.
7.
HanL.MingzhuW.PeterK.-Y. W.JackC. P. C. (2020). Full body pose estimation of construction equipment using computer vision and deep learning techniques. Automation in Construction, 110, 103016. https://doi.org/10.1016/j.autcon.2019.103016
HuiM.TurgayC.HengchaoL. (2021). FER-YOLO: Detection and classification based on facial expressions. In Image and Graphics (pp. 28–39). Springer. https://doi.org/10.1007/978-3-030-87355-4_3
10.
JoonOhS.SangUkH.SangHyunL.HyoungkwanK. (2015). Computer vision techniques for construction safety and health monitoring. Advanced Engineering Informatics, 29(2), 239–251. https://doi.org/10.1016/j.aei.2015.02.001
11.
KochC.DoychevaK.KasiV.AkinciB.FieguthP. (2015). A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure. Advanced Engineering Informatics, 29(2), 196–210. https://doi.org/10.1016/j.aei.2015.01.008
12.
LanW.DangJ.WangY.WangS. (2018). Pedestrian Detection Based on YOLO Network Model. In International conference on mechatronics and automation (ICMA) (pp. 1547–1551).
13.
LiQ.DingX.WangX.ChenL.SonJ.SongJ.-Y. (2021). Detection and identification of moving objects at busy traffic road based on YOLO v4. Journal of the Institute of Webcasting, Internet and Telecommunication, 21(1), 141–148. https://doi.org/10.7236/JIIBC.2021.21.1.141
14.
LiangS.WuH.ZhenL.HuaQ.GargS.KaddoumG.HassanM. M.YuK. (2022). Edge YOLO: Real-time intelligent object detection system based on edge-cloud cooperation in autonomous vehicles. IEEE Transactions on Intelligent Transportation Systems, 23(12), 25345–25360. https://doi.org/10.1109/TITS.2022.3158253
15.
LippiM.BonucciN.CarpioR. F.ContariniM.SperanzaS.GasparriA. (2021). A YOLO-based pest detection system for precision agriculture. In 2021 29th Mediterranean Conference on Control and Automation (MED) (pp. 342–347). IEEE. https://doi.org/10.1109/MED51440.2021.9480344
16.
MingyuanZ.MiZ.XuefengZ. (2020). Recognition of high-risk scenarios in building construction based on image semantics. Journal of Computing in Civil Engineering, 34(4), 04020019. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000900
17.
NurinM. A. A. D.SyamimiA. K.ShuzlinaA. R.SofianitaM. (2022). Object detection for autonomous vehicles with sensor-based technology using YOLO. International Journal of Intelligent Systems and Applications in Engineering, 10(1), 129–134. https://doi.org/10.18201/ijisae.2022.276
18.
PatroK. S. K.YadavV. K.BhartiV. S.SharmaA.SharmaA. (2023). Fish detection in underwater environments using deep learning. National Academy Science Letters, 46(5), 407–412. https://doi.org/10.1007/s40009-023-01265-4
RoyA. M.BhaduriJ. (2023). DenseSPH-YOLOv5: An automated damage detection model based on DenseNet and swin-transformer prediction head-enabled YOLOv5 with attention mechanism. Advanced Engineering Informatics, 56, 102007. https://doi.org/10.1016/j.aei.2023.102007
21.
RuiD.HuiD.MaoT.YichuanD.JiaruiL. (2022). SODA: A large-scale open site object detection dataset for deep learning in construction. Automation in Construction, 142, 104499. https://doi.org/10.1016/j.autcon.2022.104499
22.
ShubhamS.AshwinK.VikramG. (2018). YOLO Based human action recognition and localization. Procedia Computer Science, 133, 831–838. https://doi.org/10.1016/j.procs.2018.07.112
23.
TianY.YangG.WangZ.WangH.LiE.LiangZ. (2019). Apple detection during different growth stages in orchards using the improved YOLO-V3 model. Computers and Electronics in Agriculture, 157, 417–426. https://doi.org/10.1016/j.compag.2019.01.012
24.
VargheseR.SambathM. (2024). YOLOv8: A Novel Object Detection Algorithm with Enhanced Performance and Robustness. In 2024 International conference on advances in data engineering and intelligent computing systems (ADICS), Chennai, India (pp. 1–6). https://doi.org/10.1109/ADICS58448.2024.10533619
25.
VenkatesanD.KannanK.BalachandarS. R. (2016). A new genetic algorithm for time dependent combinatorial optimization problem. National Academy Science Letters, 39, 207–211. https://doi.org/10.1007/s40009-016-0433-5
26.
WangH. (2024). Detection of Personal Protective Equipment (PPE) using an Anchor Free-Convolutional Neural Network. International Journal of Advanced Computer Science and Applications (IJACSA), 15(2). https://doi.org/10.14569/IJACSA.2024.0150239
27.
WeiliF.LieyunD.HanbinL.PeterE. D. L. (2018). Falls from heights: A computer vision-based approach for safety harness detection. Automation in Construction, 91, 53–61. https://doi.org/10.1016/j.autcon.2018.02.018
28.
XuezhiX.NingL.GuoX.WangS.ElS. A. (2018). Engineering vehicles detection based on modified faster R-CNN for power grid surveillance. Sensors, 18(7), 2258. https://doi.org/10.3390/s18072258
29.
YawaleN.SahuN.KhalsaN. (2024). A multimodal feature representation model for transfer-learning-based identification of images. National Academy Science Letters, 47, 663–669. https://doi.org/10.1007/s40009-024-01402-7
30.
YichuanD.HaoH.HanL.HuiD. (2017). Automatic indoor progress monitoring using BIM and computer vision. In The 7th international conference on construction engineering and project management.
31.
ZaolinP.ChengS.YichuanD.JackC. (2021). Video2Entities: A computer vision-based entity extraction framework for updating the architecture, engineering and construction industry knowledge graphs. Automation in Construction, 125, 103617. https://doi.org/10.1016/j.autcon.2021.103617
32.
ZhangM.ShiR.YangZ. (2020). A critical review of vision-based occupational health and safety monitoring of construction site workers. Safety Science, 126, 104658. https://doi.org/10.1016/j.ssci.2020.104658
33.
ZhenY.YongboY.MingyuanZ.XuefengZ.YangZ.BoquanT. (2019). Safety Distance Identification for Crane Drivers Based on Mask R-CNN. Sensors (Basel, Switzerland), 19(18), 3994. https://doi.org/10.3390/s19183994
34.
ZhengY.ZhangH. (2022). Video analysis in sports by lightweight object detection network under the background of sports industry development. Computational Intelligence and Neuroscience, 1–10, Article 3844770. https://doi.org/10.1155/2022/3844770
