Object Detection of Motor Vehicle Suspension Systems Based on the YOLOv8 Algorithm: Supporting Sustainable Industry and Innovation (SDG 9)

Helmi  Wibowo; Nurul Muzakki Rihhadatul  ‘Aisy; Muhammad Iman Nur  Hakim; Mokhammad Rifqi  Tsani; Setya  Wijayanta

doi:10.63230/jocsis.3.2.192

Authors

Helmi Wibowo Politeknik Keselamatan Transportasi Jalan Author
Nurul Muzakki Rihhadatul ‘Aisy Politeknik Keselamatan Transportasi Jalan Author
Muhammad Iman Nur Hakim Politeknik Keselamatan Transportasi Jalan Author
Mokhammad Rifqi Tsani Politeknik Keselamatan Transportasi Jalan Author
Setya Wijayanta Politeknik Keselamatan Transportasi Jalan Author

DOI:

https://doi.org/10.63230/jocsis.3.2.192

Keywords:

Automotive inspection, Computer vision, Deep learning, Object detection, Sustainable industry, Vehicle suspension system, YOLOv8 algorithm

Abstract

Objective: To develop an object detection system for identifying motor vehicle suspension system components using the YOLOv8 algorithm. Specifically, this study focused on improving the efficiency and accuracy of undercarriage inspection processes, which are commonly conducted manually and require technical knowledge to recognize suspension components and detect potential damage. This research contributes to automotive inspection innovation and supports the development of sustainable industrial technology in accordance with Sustainable Development Goal 9 (Industry, Innovation, and Infrastructure). Method: The study employed a computer vision-based approach using the YOLOv8 object detection algorithm for recognizing suspension system components in motor vehicles. The dataset consisted of 1000 suspension system images collected from mandatory vehicle inspection activities at motor vehicle testing facilities. Results: The results showed that the YOLOv8-based detection model could identify suspension system components with an accuracy of up to 95%. Furthermore, the trained model successfully detected oil leakage damage on shock absorber components with an accuracy of 92%. The evaluation results indicate that the proposed system can effectively recognize suspension components under different inspection conditions and provide reliable assistance for vehicle undercarriage inspection processes. Novelty: The study provides a novel implementation of the YOLOv8 deep learning algorithm for automated suspension system inspection in motor vehicles by integrating computer vision technology into the vehicle testing process. The developed system contributes to automotive technology innovation and supports the advancement of smart inspection infrastructure in line with SDG 9 (Industry, Innovation, and Infrastructure).

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