Transformer-based Segmentation

This paper introduces ALBERT, an instance segmentation model designed specifically for comprehensive car damage and part segmentation. Leveraging the power of Bidirectional Encoder Representations, ALBERT incorporates advanced localization mechanisms to accurately identify and differentiate between real and fake damages as well as segment individual car parts. The model is trained on a large-scale, richly annotated automotive dataset, categorizing damage into 26 types, identifying 7 fake damage variants, and segmenting 61 distinct car parts. Our approach demonstrates strong performance in both segmentation accuracy and damage classification, paving the way for intelligent automotive inspection and assessment applications. This work not only contributes a powerful tool for automated vehicle inspection but also lays the groundwork for future research in intelligent automotive diagnostics, safety evaluation, and insurance claim automation, with significant implications for both industry and research communities.

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.