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A New Deep Learning Based Damage Classification Pipeline for Car Glass

Year 2025, Volume: 11 Issue: 3, 210 - 223, 30.09.2025

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.

Ethical Statement

No approval from the Board of Ethics is required.

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There are 24 citations in total.

Details

Primary Language English
Subjects Computer Vision, Deep Learning
Journal Section Research Article
Authors

Ömer Küllü 0000-0002-7247-4120

Anıl Çelik 0000-0002-4208-5570

Pınar Ece Aktan Hatipoğlu 0009-0005-3479-7753

Mehmet Kıvılcım Keleş 0000-0001-5358-8301

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 Issue: 3

Cite

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.
AMA Küllü Ö, Çelik A, Aktan Hatipoğlu PE, Keleş MK. A New Deep Learning Based Damage Classification Pipeline for Car Glass. JARNAS. September 2025;11(3):210-223.
Chicago Küllü, Ömer, Anıl Çelik, Pınar Ece Aktan Hatipoğlu, and Mehmet Kıvılcım Keleş. “A New Deep Learning Based Damage Classification Pipeline for Car Glass”. Journal of Advanced Research in Natural and Applied Sciences 11, no. 3 (September 2025): 210-23.
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 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, 2025.
ISNAD Küllü, Ömer et al. “A New Deep Learning Based Damage Classification Pipeline for Car Glass”. Journal of Advanced Research in Natural and Applied Sciences 11/3 (September2025), 210-223.
JAMA 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, 2025, pp. 210-23.
Vancouver Küllü Ö, Çelik A, Aktan Hatipoğlu PE, Keleş MK. A New Deep Learning Based Damage Classification Pipeline for Car Glass. JARNAS. 2025;11(3):210-23.


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