A Comparative Analysis of Accuracy–Computational Efficiency Trade-offs in YOLOv8, YOLOv9, and YOLOv11 for Underbody Vehicle Component Detection

Helmi Wibowo; Moch. Aziz Kurniawan; Jihan Luis

doi:10.17509/edsence.v8i1.94803

A Comparative Analysis of Accuracy–Computational Efficiency Trade-offs in YOLOv8, YOLOv9, and YOLOv11 for Underbody Vehicle Component Detection

Helmi Wibowo, Moch. Aziz Kurniawan, Jihan Luis

Abstract

Traffic accidents are often caused by vehicle technical failures, making periodic inspections crucial. This study aims to implement YOLOv8, YOLOv9, and YOLOv11 algorithms on a camera-equipped robotic platform to detect powertrain system components located underneath vehicles. A quantitative experimental method was employed by comparing the performance of the three YOLO versions in detecting components such as the transmission, universal joint, and propeller shaft. The dataset consisted of 1,000 images collected from pick-up vehicles, processed using the Roboflow platform, and divided into training, validation, and testing sets. The results indicate that YOLOv9 achieved the highest classification accuracy, with an mAP@50 of 96.6%, while YOLOv11 demonstrated superior computational efficiency with an inference speed of 13.5 ms per image. An optimal dataset size of 500 images was found to provide a balance between accuracy and efficiency. This study contributes to the development of transparent and real-time AI-based vehicle inspection systems.

Keywords

YOLO; Object Detection; Vehicle Inspection; Artificial Intelligence (AI); Powertrain System

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DOI: https://doi.org/10.17509/edsence.v8i1.94803