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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Information Technology and Computer Science Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Jurnal Sistem Informasi, Teknologi Informasi, dan Edukasi Sistem Informasi (JUST-SI)
Setya Perdana, Rizal
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Combining Inference Results of YOLOv8 and Faster R-CNN using Weighted Boxes Fusion for Car’s Underbody Quality Inspection (Study Case On Automotive Company In Indonesia) Alfons Abilo, Nelson; Indriati; Setya Perdana, Rizal
Journal of Information Technology and Computer Science Vol. 10 No. 1: April 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025101918

Abstract

The automotive industry in Indonesia has gradually experienced rapid development over time, leading to intense competition among automotive companies. In such a competitive environment, the quality inspection process is a critical aspect of the automotive manufacturing industry. One significant issue in the quality inspection process is human error. The Fourth Industrial Revolution offers solutions through computer vision technology to improve production quality. YOLOv8 is a real-time object detection algorithm that offers fast inference and good background reduction, but it is less effective for small and low-contrast objects. Conversely, Faster R-CNN has high confidence scores but slow inference capabilities. Company XYZ has a visual inspection system using YOLOv8, with a mAP50@95 value of 74%. However, the confidence scores produced often do not meet the company's standard threshold, which requires a confidence score above 90%. Therefore, a study was conducted to combine the inference results of YOLOv8 and Faster R-CNN using the weighted boxes fusion method to enhance the inference results of YOLOv8. The study results showed an increase in mAP when combining the inferences of the two models, compared to when each model performed inference individually – there was a 3.8% increase compared to YOLOv8's performance and a 5.5% increase compared to Faster R-CNN's performance.  
Co-Authors Abdul Jabbar Afrialdy, Firman Alfons Abilo, Nelson Aryo Pinandito Candra Dewi Dian Eka Ratnawati Firdaus, Nada Hamzaturrazak, Multohadi Indriati Jibril Averroes, Muhammad Lailil Muflikhah Pandu Adikara, Putra Rafif, Sulthan RR. Ella Evrita Hestiandari Salsabila Sangdiva Laksono, Khansa Susanto, Dominicus Christian Bagus Syemael Siahaan, Arion