A New Deep Learning Based Damage Classification Pipeline for Car Glass

Intelligent damage evaluation is a popular domain of applied Artificial Intelligence (AI) in the insurance industry. Car glass damage is a common insurance claim. Accurately identifying the nature of glass damage is essential, as it directly affects the decision between repair and replacement, which has different safety and cost implications. This distinction is crucial to ensure vehicle safety and effective claim processing. This paper proposes a deep learning (DL) enhanced approach to reduce the claim process time by automatically detecting damage in car glass. The primary objective of this study is to assess whether glass damage is suitable for repair or necessitates total replacement. Owing to variations in the image zoom levels for damaged glass, our method incorporates a branched pathway to handle these differences effectively. Our pipeline employs both classification and segmentation methods. The proposed approach begins by filtering the images. Based on the results, the image is fed directly to the damage classification or subjected to further processing before classification. Furthermore, ensemble and fusion techniques were employed to improve overall efficiency. The ensemble-based strategy achieved an F1 score of 0.99 in damage classification, outperforming other approaches.