ALBERT: Advanced Localization and Bidirectional Encoder Representations from Transformers for Automotive Damage Evaluation
This addresses automotive inspection and assessment applications, but appears incremental as it builds on existing transformer-based methods for a specific domain.
The paper tackles comprehensive car damage and part segmentation by introducing ALBERT, an instance segmentation model that accurately identifies real vs. fake damages and segments individual car parts, achieving strong performance in segmentation accuracy and damage classification.
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.