Real-Time Bolt Defect Detection with Artificial Intelligence in Industrial Quality Control Applications: Comparative Evaluation of YOLO Architectures

Cem Özkurt; Özkan Canay; Polat Üzelge; Ahmet Dursun; Mücahit Soyaslan

doi:10.31127/tuje.1781823

Real-Time Bolt Defect Detection with Artificial Intelligence in Industrial Quality Control Applications: Comparative Evaluation of YOLO Architectures

Abstract

This study presents an artificial intelligence-based system for the real-time and high-accuracy detection of defects in bolts moving on a production line conveyor belt. Bolt defects are critical for product safety and production quality, while manual inspection methods remain insufficient due to their time-consuming nature, susceptibility to human error, and limited reliability. Although various approaches have been proposed in the literature, existing methods are limited in terms of small-object detection, real-time performance, and industrial integration. To address these limitations, a deep learning-based defect detection mechanism was developed and comparatively evaluated using the YOLOv9, YOLOv10, and YOLO11 architectures, with emphasis on low hardware requirements, ease of integration, and real-time capability. A custom dataset of 12,075 high-resolution conveyor belt bolt images was constructed and used to train and validate all model variants. Experimental results demonstrate that all three architectures achieved over 99% mAP50, with the YOLO11x model reaching 99.39% mAP50 and 90.90% mAP50-95, closely matched by YOLOv10x at 99.39% mAP50 and 90.91% mAP50-95. Considering both detection accuracy and inference speed, YOLO11x provides the most favorable balance for production line integration, while YOLOv10x stands out as a competitive alternative with comparable accuracy and lower computational cost. The proposed system offers a faster, more reliable, and easily integrable solution than manual inspection, with strong potential for adoption in high-precision industrial sectors such as automotive, aerospace, and defense.

Keywords

References

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Details

Primary Language

English

Subjects

Computer Software

Journal Section

Research Article

Authors

Cem Özkurt ^*
0000-0002-1251-7715
Türkiye

Özkan Canay
0000-0001-7539-6001
Türkiye

Polat Üzelge
0009-0000-6320-3794
Türkiye

Ahmet Dursun
0009-0007-9674-8257
Türkiye

Mücahit Soyaslan
0000-0001-6658-5169
Türkiye

Publication Date

July 6, 2026

Submission Date

September 11, 2025

Acceptance Date

February 17, 2026

Published in Issue

Year 2026 Volume: 10 Number: 3

DOI

https://doi.org/10.31127/tuje.1781823

IZ

https://izlik.org/JA82SW54AY

Cite

RIS / Bibtex

APA

Özkurt, C., Canay, Ö., Üzelge, P., Dursun, A., & Soyaslan, M. (2026). Real-Time Bolt Defect Detection with Artificial Intelligence in Industrial Quality Control Applications: Comparative Evaluation of YOLO Architectures. Turkish Journal of Engineering, 10(3), 919-935. https://doi.org/10.31127/tuje.1781823

AMA

1.Özkurt C, Canay Ö, Üzelge P, Dursun A, Soyaslan M. Real-Time Bolt Defect Detection with Artificial Intelligence in Industrial Quality Control Applications: Comparative Evaluation of YOLO Architectures. TUJE. 2026;10(3):919-935. doi:10.31127/tuje.1781823

Chicago

Özkurt, Cem, Özkan Canay, Polat Üzelge, Ahmet Dursun, and Mücahit Soyaslan. 2026. “Real-Time Bolt Defect Detection With Artificial Intelligence in Industrial Quality Control Applications: Comparative Evaluation of YOLO Architectures”. Turkish Journal of Engineering 10 (3): 919-35. https://doi.org/10.31127/tuje.1781823.

EndNote

Özkurt C, Canay Ö, Üzelge P, Dursun A, Soyaslan M (July 1, 2026) Real-Time Bolt Defect Detection with Artificial Intelligence in Industrial Quality Control Applications: Comparative Evaluation of YOLO Architectures. Turkish Journal of Engineering 10 3 919–935.

IEEE

[1]C. Özkurt, Ö. Canay, P. Üzelge, A. Dursun, and M. Soyaslan, “Real-Time Bolt Defect Detection with Artificial Intelligence in Industrial Quality Control Applications: Comparative Evaluation of YOLO Architectures”, TUJE, vol. 10, no. 3, pp. 919–935, July 2026, doi: 10.31127/tuje.1781823.

ISNAD

Özkurt, Cem - Canay, Özkan - Üzelge, Polat - Dursun, Ahmet - Soyaslan, Mücahit. “Real-Time Bolt Defect Detection With Artificial Intelligence in Industrial Quality Control Applications: Comparative Evaluation of YOLO Architectures”. Turkish Journal of Engineering 10/3 (July 1, 2026): 919-935. https://doi.org/10.31127/tuje.1781823.

JAMA

1.Özkurt C, Canay Ö, Üzelge P, Dursun A, Soyaslan M. Real-Time Bolt Defect Detection with Artificial Intelligence in Industrial Quality Control Applications: Comparative Evaluation of YOLO Architectures. TUJE. 2026;10:919–935.

MLA

Özkurt, Cem, et al. “Real-Time Bolt Defect Detection With Artificial Intelligence in Industrial Quality Control Applications: Comparative Evaluation of YOLO Architectures”. Turkish Journal of Engineering, vol. 10, no. 3, July 2026, pp. 919-35, doi:10.31127/tuje.1781823.

Vancouver

1.Cem Özkurt, Özkan Canay, Polat Üzelge, Ahmet Dursun, Mücahit Soyaslan. Real-Time Bolt Defect Detection with Artificial Intelligence in Industrial Quality Control Applications: Comparative Evaluation of YOLO Architectures. TUJE. 2026 Jul. 1;10(3):919-35. doi:10.31127/tuje.1781823