Аннотации:
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.
