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

TL;DR

ALBERT is a specialized instance segmentation model utilizing bidirectional transformers for precise automotive damage detection, differentiation between real and fake damages, and detailed car part segmentation, trained on a comprehensive automotive dataset.

Contribution

This paper presents ALBERT, a novel transformer-based model tailored for automotive damage evaluation and part segmentation, with advanced localization and classification capabilities.

Findings

High segmentation accuracy on automotive damage

Effective differentiation between real and fake damages

Robust performance across multiple damage types and car parts

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.