Improving YOLO Detection Performance of Autonomous Vehicles in Adverse Weather Conditions Using Metaheuristic Algorithms

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dc.authoridALTUN, Yusuf/0000-0002-2099-0959en_US
dc.authoridPARLAK (PhD), CEVAHIR/0000-0002-5500-7379en_US
dc.authoridozcan, ibrahim/0000-0001-9471-5119en_US
dc.authorscopusid59217106500en_US
dc.authorscopusid25031391400en_US
dc.authorscopusid55807221400en_US
dc.authorwosidALTUN, Yusuf/AAA-9929-2020en_US
dc.contributor.authorOzcan, Ibrahim
dc.contributor.authorAltun, Yusuf
dc.contributor.authorParlak, Cevahir
dc.date.accessioned2024-08-23T16:03:37Z
dc.date.available2024-08-23T16:03:37Z
dc.date.issued2024en_US
dc.departmentDüzce Üniversitesien_US
dc.description.abstractDespite the rapid advances in deep learning (DL) for object detection, existing techniques still face several challenges. In particular, object detection in adverse weather conditions (AWCs) requires complex and computationally costly models to achieve high accuracy rates. Furthermore, the generalization capabilities of these methods struggle to show consistent performance under different conditions. This work focuses on improving object detection using You Only Look Once (YOLO) versions 5, 7, and 9 in AWCs for autonomous vehicles. Although the default values of the hyperparameters are successful for images without AWCs, there is a need to find the optimum values of the hyperparameters in AWCs. Given the many numbers and wide range of hyperparameters, determining them through trial and error is particularly challenging. In this study, the Gray Wolf Optimizer (GWO), Artificial Rabbit Optimizer (ARO), and Chimpanzee Leader Selection Optimization (CLEO) are independently applied to optimize the hyperparameters of YOLOv5, YOLOv7, and YOLOv9. The results show that the preferred method significantly improves the algorithms' performances for object detection. The overall performance of the YOLO models on the object detection for AWC task increased by 6.146%, by 6.277% for YOLOv7 + CLEO, and by 6.764% for YOLOv9 + GWO.en_US
dc.description.sponsorshipDuzce University Scientific Research Projects Coordination Office [BAP-2020.06.01.1060]en_US
dc.description.sponsorshipThis research was funded by Duzce University Scientific Research Projects Coordination Office with the Scientific Research Project grant number BAP-2020.06.01.1060.en_US
dc.identifier.doi10.3390/app14135841
dc.identifier.issn2076-3417
dc.identifier.issue13en_US
dc.identifier.scopus2-s2.0-85198479179en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.3390/app14135841
dc.identifier.urihttps://hdl.handle.net/20.500.12684/13828
dc.identifier.volume14en_US
dc.identifier.wosWOS:001269153900001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofApplied Sciences-Baselen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAROen_US
dc.subjectCLEOen_US
dc.subjectDAWN dataseten_US
dc.subjectdeep learningen_US
dc.subjectGWOen_US
dc.subjectobject detectionen_US
dc.subjectRTTS dataseten_US
dc.subjectYOLOv5en_US
dc.subjectYOLOv7en_US
dc.subjectYOLOv9en_US
dc.subjectGrey Wolf Optimizeren_US
dc.titleImproving YOLO Detection Performance of Autonomous Vehicles in Adverse Weather Conditions Using Metaheuristic Algorithmsen_US
dc.typeArticleen_US

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