用结构先验和领域知识提升汽车损伤分割精度与鲁棒性
SLICK: Selective Localization and Instance Calibration for Knowledge-Enhanced Car Damage Segmentation in Automotive Insurance
- 通过结构先验引导高分辨率语义骨干，实现遮挡下的精准部件分割
- 在复杂街景中动态聚焦损伤区域，提升细粒度检测能力
- 融合真实与合成数据，有效处理罕见损伤案例，适合保险理赔场景
我们提出SLICK，一种新颖的汽车损伤分割框架，利用结构先验和领域知识应对真实世界汽车检测挑战。SLICK包含五个关键组件：(1) 基于结构先验引导的高分辨率语义骨干，实现遮挡、形变或漆面脱落情况下的精准部件分割；(2) 局部化注意力模块，动态聚焦损伤区域，提升复杂街景中的细粒度损伤检测；(3) 实例敏感精修头，结合全景线索与形状先验，分离重叠或邻近部件，实现精确边界对齐；(4) 跨通道校准，通过多尺度通道注意力增强划痕、凹陷等微弱损伤信号，抑制反光、贴纸等噪声；(5) 知识融合模块，整合合成碰撞数据、部件几何信息与真实保险数据集，提升泛化能力并有效处理罕见情况。在大规模汽车数据集上的实验表明，SLICK在分割性能、鲁棒性及实际应用性方面均表现优异。
原文摘要 · Abstract (English)
We present SLICK, a novel framework for precise and robust car damage segmentation that leverages structural priors and domain knowledge to tackle real-world automotive inspection challenges. SLICK introduces five key components: (1) Selective Part Segmentation using a high-resolution semantic backbone guided by structural priors to achieve surgical accuracy in segmenting vehicle parts even under occlusion, deformation, or paint loss; (2) Localization-Aware Attention blocks that dynamically focus on damaged regions, enhancing fine-grained damage detection in cluttered and complex street scenes; (3) an Instance-Sensitive Refinement head that leverages panoptic cues and shape priors to disentangle overlapping or adjacent parts, enabling precise boundary alignment; (4) Cross-Channel Calibration through multi-scale channel attention that amplifies subtle damage signals such as scratches and dents while suppressing noise like reflections and decals; and (5) a Knowledge Fusion Module that integrates synthetic crash data, part geometry, and real-world insurance datasets to improve generalization and handle rare cases effectively. Experiments on large-scale automotive datasets demonstrate SLICK's superior segmentation performance, robustness, and practical applicability for insurance and automotive inspection workflows.
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