ScholarWorks@중앙대학교

초록

The segmentation of damaged areas in vehicle images is essential for practical applications such as automated insurance processing, vehicle resale evaluation, and maintenance support. Damage patterns often appear in different forms and sizes, and exhibit irregular shapes, which makes consistent segmentation difficult. These characteristics tend to occur simultaneously in real-world environments, resulting in conditions referred to as multiple multi-scale jagged masks. To address this problem, this study designed a model to separate overlapping regions, extract features across multiple spatial resolutions, and enhance uncertain boundaries through attention-guided refinement. The proposed model was evaluated on a large-scale public dataset of annotated vehicle surface damage images using four standard metrics: intersection over union, F1-Score, precision, and recall. Comparative experiments with damage-specific and general-purpose segmentation models revealed that the proposed model achieved the highest performance across all metrics and maintained stability across repeated trials.

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