SLICK: Selective Localization and Instance Calibration for Knowledge-Enhanced Car Damage Segmentation in Automotive Insurance

2025-06-12

AI Summary

Addressing challenges in real-world road scenarios—including occlusion, deformation, paint loss, part overlap, and fine-scale scratches/dents that are difficult to segment—this paper proposes a structure- and knowledge-cooperative automotive damage segmentation method. The approach introduces a selective part segmentation module, a localization-aware attention mechanism, an instance-sensitive refinement head, a cross-channel calibration module, and a multi-source knowledge fusion module. It integrates a high-resolution semantic backbone, dynamic localization attention, pan-instance-guided shape prior modeling, and a joint training strategy leveraging synthetic collision data and geometric priors. Evaluated on a large-scale automotive dataset, the method achieves significant improvements in boundary alignment accuracy and overall segmentation performance: detection rates for fine-scale damages (scratches/dents) increase by 23.6%, and noise robustness reaches state-of-the-art (SOTA) levels.

Technology Category

• Computer Vision: Segmentation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

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.

Problem

• Precise car damage segmentation under occlusion and deformation
• Enhanced fine-grained damage detection in complex street scenes
• Improved generalization using synthetic and real-world insurance data

Innovation

• Selective Part Segmentation with high-resolution semantic backbone
• Localization-Aware Attention blocks for damaged regions
• Instance-Sensitive Refinement head with panoptic cues