Vol.15, No.2, May 2026.                                                                                                                                                                          ISSN: 2217-8309

                                                                                                                                                                                                                        eISSN: 2217-8333

TEM Journal

TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS

Association for Information Communication Technology Education and Science

Comparative Study of YOLOv8 Nano and YOLOv8 Small for Screw Defect Detection

Eri Prasetyo Wibowo, Yoga Panji Perdana Nugraha, Nur Sultan Salahuddin, Sri Nawangsari, Ias Sri Wahyuni

© 2026 Yoga Panji Perdana Nugraha, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)

Citation Information: TEM Journal. Volume 15, Issue 2, Pages 1007-1015, ISSN 2217-8309, DOI: 10.18421/TEM152-05, May 2026.

Received: 11 July 2025.
Revised: 24 November 2025.
Accepted: 13 December 2025.
Published: 27 May 2026.

Abstract:

This study aims to develop a model to detect screw defects. The screws detected were single screws, multiple screws, and multiple stacked screws. The technology that can be applied to industries to improve product inspection performance is deep learning. The technology that can be applied to industries to improve product inspection performance is deep learning. The algorithm used is YOLOv8. This study presents an original approach by comparing the performance of YOLOv8n and YOLOv8s for screw defect detection using an Oriented Bounding Box (OBB) configuration. The findings indicate that the YOLOv8s model with Oriented Bounding Box (OBB) configuration outperforms YOLOv8n in detecting screw defects. YOLOv8s achieved a mAP50 of 0.832 and mAP50-95 of 0.609, higher than those of YOLOv8n, which are 0.785 and 0.595, respectively. Moreover, the use of training configurations such as early stopping and larger image sizes proved to enhance training efficiency without compromising model accuracy. The integration of OBB in YOLOv8s for precision industrial object detection is a novel contribution. A comprehensive evaluation was conducted using MAP metrics, confusion matrix, and F1-confidence curves.

Keywords – Artificial Intelligence, deep learning, YOLOv8 OBB, defect detection, screw.

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