ALBERT: Advanced Localization and Bidirectional Encoder Representations from Transformers for Automotive Damage Evaluation

2025-06-12

AI Summary

This study addresses the joint modeling challenge of authentic/forged damage discrimination and fine-grained vehicle body part segmentation for intelligent vehicle damage assessment. We propose the first unified end-to-end framework integrating a bidirectional Transformer encoder with an adaptive localization attention mechanism, supporting 26 classes of authentic damage recognition, 7 classes of forged damage classification, and 61-class instance-level part segmentation. Leveraging multi-task joint training and a large-scale, meticulously annotated dataset—including forged damage samples—our method achieves 82.3% mAP for part segmentation and 94.1% accuracy for damage classification on a custom benchmark, significantly outperforming Mask R-CNN and Swin-InstanceSegmenter. The core innovation lies in the first integration of bidirectional semantic representation, precise localization capability, and hierarchical joint understanding of damage and parts within a single instance segmentation architecture.

Technology Category

• Computer Vision: Segmentation
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

This paper introduces ALBERT, an instance segmentation model specifically designed 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 that categorizes damage into 26 types, identifies 7 fake damage variants, and segments 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.

Problem

• Accurate segmentation of car damages and parts
• Differentiation between real and fake automotive damages
• Classification of 26 damage types and 61 car parts

Innovation

• Bidirectional Encoder Representations for segmentation
• Advanced localization for damage identification
• Large-scale annotated dataset for training