ALBERT: Advanced Localization and Bidirectional Encoder Representations from Transformers for Automotive Damage Evaluation
Year: 2025
Type: preprint
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.
Source: arXiv (Cornell University)
Author: Panboonyuen, Teerapong
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4417353158
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