We propose SLICK, a novel and efficient framework for high-precision car damage segmentation, designed for real-world deployment in automotive insurance and inspection workflows. SLICK introduces five synergistic components, selective part segmentation guided by structural priors, localization-aware attention to highlight fine-grained damage, instance-sensitive refinement for precise boundary separation, cross-channel calibration to amplify subtle cues like scratches and dents, and a knowledge fusion module that integrates synthetic crash data, part geometry, and annotated insurance datasets. Trained using a teacher–student distillation strategy with ALBERT as the teacher, SLICK retains high segmentation fidelity while achieving up to 7× faster inference. Extensive experiments on large-scale automotive datasets demonstrate SLICK’s superior accuracy, generalization, and runtime efficiency—making it ideal for real-time, high-stakes applications in insurance automation and vehicle inspection.
