Car Object Detection: Comparative Analysis of YOLOv9 and YOLOv10 Models

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dc.contributor.authorKara Ardaç, Fatma Betül
dc.contributor.authorErdoǧmuş, Pakize
dc.date.accessioned2025-10-11T20:45:23Z
dc.date.available2025-10-11T20:45:23Z
dc.date.issued2024
dc.departmentDüzce Üniversitesien_US
dc.descriptionIEEE SMC; IEEE Turkiye Sectionen_US
dc.description2024 Innovations in Intelligent Systems and Applications Conference -- ASYU 2024 -- Ankara -- AX6204562en_US
dc.description.abstractThe field of computer vision known as car detection technology involves the identification and tracking of vehicles in images or video frames. Car detection is a widely utilized technology in various applications, including traffic control, parking management, and security systems. Recently, you YOLO (Only Look Once) based object detection methods have attracted significant attention in autonomous systems such as vehicle detection and parking systems due to their ability to perform real-time object detection. In this study, the performance of two state-of-the-art YOLO versions, YOLOv9 and YOLOv10, is evaluated using a vehicle image dataset. The experimental studies indicate that YOLOv10 has superior detection performance compared to YOLOv9. Additionally, both models have been demonstrated to achieve high accuracy in terms of training time and extraction speed. In particular, YOLOv10 demonstrated superior performance to YOLOv9, with recall ranging from 90% to 94% and precision rates between 94% and 96%. Furthermore, the YOLOv9 model exhibited faster training times and lower inference times, rendering it more suitable for real-time applications. Despite the challenges, including false positives and false negatives, our findings can contribute to the improvement of the accuracy and efficiency of car detection, thereby enhancing traffic control, parking management, and security systems. © 2024 Elsevier B.V., All rights reserved.en_US
dc.identifier.doi10.1109/ASYU62119.2024.10756955
dc.identifier.isbn9798350379433
dc.identifier.scopus2-s2.0-85213332791en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1109/ASYU62119.2024.10756955
dc.identifier.urihttps://hdl.handle.net/20.500.12684/21324
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzKA_Scopus_20250911
dc.subjectCar Objecten_US
dc.subjectDetectionen_US
dc.subjectYolov10en_US
dc.subjectYolov9en_US
dc.subjectAutomobilesen_US
dc.subjectHighway Administrationen_US
dc.subjectImage Enhancementen_US
dc.subjectIntelligent Systemsen_US
dc.subjectStreet Traffic Controlen_US
dc.subjectCar Detectionen_US
dc.subjectCar Objecten_US
dc.subjectComparative Analyzesen_US
dc.subjectDetectionen_US
dc.subjectObjects Detectionen_US
dc.subjectParking Managementen_US
dc.subjectPerformanceen_US
dc.subjectTraining Timeen_US
dc.subjectYolov10en_US
dc.subjectYolov9en_US
dc.subjectParkingen_US
dc.titleCar Object Detection: Comparative Analysis of YOLOv9 and YOLOv10 Modelsen_US
dc.typeConference Objecten_US

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