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

Teerapong Panboonyuen
2025
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Abstract

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.

Type
Preprint
Publication
In arXiv:2506.10524 [cs.CV]

Automotive Damage Detection instance-segmentation Vision Transformers Bidirectional Encoder Representations Fake Damage Classification Car Part Segmentation Deep Learning for Transportation AI for Auto Insurance Car Insurance Automation Intelligent Vehicle Inspection ALBERT Model Transformer-based Segmentation Automotive Visual AI Insurance Fraud Detection
Teerapong Panboonyuen
Teerapong Panboonyuen

My research focuses on leveraging advanced machine intelligence techniques, specifically computer vision, to enhance semantic understanding, learning representations, visual recognition, and geospatial data interpretation.

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