DamageNet: A Dilated Convolution Feature Pyramid Network Mask R‑CNN for Automated Car Damage Detection and Segmentation
Katayev N. Yessengaliyeva Z. Kozhamkulova Z. Bakirova Z. Abuova A. Kuandikova G.
2025Science and Information Organization
International Journal of Advanced Computer Science and Applications
2025#16Issue 5206 - 216 pp.
Automated and precise assessment of vehicle damage is critical for modern insurance processing, accident analysis, and autonomous maintenance systems. In this work, we introduce DamageNet, a unified deep instance segmentation framework that embeds a multi‑rate dilated‑convolution context module within a Feature Pyramid Network (FPN) backbone and couples it with a Region Proposal Network (RPN), RoI‑Align, and parallel heads for classification, bounding‑box regression, and pixel‑level mask prediction. Evaluated on the large‑scale VehiDE dataset comprising 5 200 high‑resolution images annotated for dents, scratches, and broken glass, DamageNet achieves a mean Average Precision (mAP) of 85.7% for damage localization and a mean Intersection over Union (mIoU) of 82.3% for segmentation, outperforming baseline Mask R‑CNN by 6.2 and 7.8 percentage points, respectively. Ablation studies confirm that the dilated‑convolution module, multi‑scale fusion in the FPN, and post‑processing refinements each contribute substantially to segmentation fidelity. Qualitative results demonstrate robust delineation of both subtle scratch lines and extensive panel deformations under diverse lighting and occlusion conditions. Although the integration of atrous convolutions introduces a modest inference overhead, DamageNet offers a significant advancement in end‑to‑end vehicle damage analysis. Future extensions will investigate lightweight dilation approximations, dynamic rate selection, and semi‑supervised learning strategies to further enhance processing speed and generalization to additional damage modalities.
Car damage detection , deep learning , dilated convolution , feature pyramid network , instance segmentation , Mask R‑CNN , semantic segmentation , vehicle damage assessment
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Kazakh National Women’s Teacher Training University, Almaty, Kazakhstan
L.N. Gumilyov Eurasian National University, Astana, Kazakhstan
Abai Kazakh National Pedagogical University, Almaty, Kazakhstan
Almaty Technological University, Almaty, Kazakhstan
Korkyt Ata Kyzylorda University, Kyzylorda, Kazakhstan
Kazakh National Research Technical University named after K.I. Satpayev, Almaty, Kazakhstan
Kazakh National Women’s Teacher Training University
L.N. Gumilyov Eurasian National University
Abai Kazakh National Pedagogical University
Almaty Technological University
Korkyt Ata Kyzylorda University
Kazakh National Research Technical University named after K.I. Satpayev
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