A New Deep Learning Based Damage Classification Pipeline for Car Glass

Ömer Küllü; Anıl Çelik; Pınar Ece Aktan Hatipoğlu; Mehmet Kıvılcım Keleş

doi:10.28979/jarnas.1651526

A New Deep Learning Based Damage Classification Pipeline for Car Glass

Abstract

Intelligent damage evaluation is a popular domain of applied Artificial Intelligence (AI) in the insurance industry. Car glass damage is a common insurance claim. Accurately identifying the nature of glass damage is essential, as it directly affects the decision between repair and replacement, which has different safety and cost implications. This distinction is crucial to ensure vehicle safety and effective claim processing. This paper proposes a deep learning (DL) enhanced approach to reduce the claim process time by automatically detecting damage in car glass. The primary objective of this study is to assess whether glass damage is suitable for repair or necessitates total replacement. Owing to variations in the image zoom levels for damaged glass, our method incorporates a branched pathway to handle these differences effectively. Our pipeline employs both classification and segmentation methods. The proposed approach begins by filtering the images. Based on the results, the image is fed directly to the damage classification or subjected to further processing before classification. Furthermore, ensemble and fusion techniques were employed to improve overall efficiency. The ensemble-based strategy achieved an F1 score of 0.99 in damage classification, outperforming other approaches.

Keywords

Ethical Statement

No approval from the Board of Ethics is required.

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Details

Primary Language

English

Subjects

Computer Vision, Deep Learning

Journal Section

Research Article

Authors

Ömer Küllü
0000-0002-7247-4120
Türkiye

Anıl Çelik
0000-0002-4208-5570
Türkiye

Pınar Ece Aktan Hatipoğlu
0009-0005-3479-7753
Türkiye

Mehmet Kıvılcım Keleş ^*
0000-0001-5358-8301
Türkiye

Early Pub Date

September 30, 2025

Publication Date

September 30, 2025

Submission Date

March 4, 2025

Acceptance Date

June 30, 2025

Published in Issue

Year 2025 Volume: 11 Number: 3

DOI

https://doi.org/10.28979/jarnas.1651526

IZ

https://izlik.org/JA42JZ87CM

Cite

RIS / Bibtex

APA

Küllü, Ö., Çelik, A., Aktan Hatipoğlu, P. E., & Keleş, M. K. (2025). A New Deep Learning Based Damage Classification Pipeline for Car Glass. Journal of Advanced Research in Natural and Applied Sciences, 11(3), 210-223. https://doi.org/10.28979/jarnas.1651526

AMA

1.Küllü Ö, Çelik A, Aktan Hatipoğlu PE, Keleş MK. A New Deep Learning Based Damage Classification Pipeline for Car Glass. JARNAS. 2025;11(3):210-223. doi:10.28979/jarnas.1651526

Chicago

Küllü, Ömer, Anıl Çelik, Pınar Ece Aktan Hatipoğlu, and Mehmet Kıvılcım Keleş. 2025. “A New Deep Learning Based Damage Classification Pipeline for Car Glass”. Journal of Advanced Research in Natural and Applied Sciences 11 (3): 210-23. https://doi.org/10.28979/jarnas.1651526.

EndNote

Küllü Ö, Çelik A, Aktan Hatipoğlu PE, Keleş MK (September 1, 2025) A New Deep Learning Based Damage Classification Pipeline for Car Glass. Journal of Advanced Research in Natural and Applied Sciences 11 3 210–223.

IEEE

[1]Ö. Küllü, A. Çelik, P. E. Aktan Hatipoğlu, and M. K. Keleş, “A New Deep Learning Based Damage Classification Pipeline for Car Glass”, JARNAS, vol. 11, no. 3, pp. 210–223, Sept. 2025, doi: 10.28979/jarnas.1651526.

ISNAD

Küllü, Ömer - Çelik, Anıl - Aktan Hatipoğlu, Pınar Ece - Keleş, Mehmet Kıvılcım. “A New Deep Learning Based Damage Classification Pipeline for Car Glass”. Journal of Advanced Research in Natural and Applied Sciences 11/3 (September 1, 2025): 210-223. https://doi.org/10.28979/jarnas.1651526.

JAMA

1.Küllü Ö, Çelik A, Aktan Hatipoğlu PE, Keleş MK. A New Deep Learning Based Damage Classification Pipeline for Car Glass. JARNAS. 2025;11:210–223.

MLA

Küllü, Ömer, et al. “A New Deep Learning Based Damage Classification Pipeline for Car Glass”. Journal of Advanced Research in Natural and Applied Sciences, vol. 11, no. 3, Sept. 2025, pp. 210-23, doi:10.28979/jarnas.1651526.

Vancouver

1.Ömer Küllü, Anıl Çelik, Pınar Ece Aktan Hatipoğlu, Mehmet Kıvılcım Keleş. A New Deep Learning Based Damage Classification Pipeline for Car Glass. JARNAS. 2025 Sep. 1;11(3):210-23. doi:10.28979/jarnas.1651526

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