YoloGA: An Evolutionary Computation Based YOLO Algorithm to Detect Personal Protective Equipment.
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| Title: | YoloGA: An Evolutionary Computation Based YOLO Algorithm to Detect Personal Protective Equipment. |
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| Authors: | Majumder, Amit1 (AUTHOR) amit.cse@nitjsr.ac.in, Chatterjee, Sumanta2 (AUTHOR) |
| Source: | Journal of Intelligent & Fuzzy Systems. Nov2025, Vol. 49 Issue 5, p1251-1264. 14p. |
| Subjects: | Evolutionary computation, Genetic algorithms, Industrial safety, Object recognition (Computer vision), Automatic tracking, Deep learning |
| Abstract: | 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Intelligent & Fuzzy Systems is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 188720403 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: YoloGA: An Evolutionary Computation Based YOLO Algorithm to Detect Personal Protective Equipment. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Majumder%2C+Amit%22">Majumder, Amit</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> amit.cse@nitjsr.ac.in</i><br /><searchLink fieldCode="AR" term="%22Chatterjee%2C+Sumanta%22">Chatterjee, Sumanta</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Intelligent+%26+Fuzzy+Systems%22">Journal of Intelligent & Fuzzy Systems</searchLink>. Nov2025, Vol. 49 Issue 5, p1251-1264. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Evolutionary+computation%22">Evolutionary computation</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Industrial+safety%22">Industrial safety</searchLink><br /><searchLink fieldCode="DE" term="%22Object+recognition+%28Computer+vision%29%22">Object recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Automatic+tracking%22">Automatic tracking</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Intelligent & Fuzzy Systems is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1177/18758967251338695 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 1251 Subjects: – SubjectFull: Evolutionary computation Type: general – SubjectFull: Genetic algorithms Type: general – SubjectFull: Industrial safety Type: general – SubjectFull: Object recognition (Computer vision) Type: general – SubjectFull: Automatic tracking Type: general – SubjectFull: Deep learning Type: general Titles: – TitleFull: YoloGA: An Evolutionary Computation Based YOLO Algorithm to Detect Personal Protective Equipment. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Majumder, Amit – PersonEntity: Name: NameFull: Chatterjee, Sumanta IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 10641246 Numbering: – Type: volume Value: 49 – Type: issue Value: 5 Titles: – TitleFull: Journal of Intelligent & Fuzzy Systems Type: main |
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