Damage Identification of Selected Car Parts Using Image Classification and Deep Learning

Abstract

This study presents the use of image classification and deep learning in the field of insurance claims and management for the identification and assessment of damaged vehicle parts. Vehicular insurance claims on require appraisers to decide the damage of the vehicles. A two-level machine learning-based system was developed to classify different car parts (front bumper, rear bumper, and car wheels), and to detect the presence of any damages. The image dataset used in the study was obtained from a Google image. This dataset is used for training and validation of the convolutional neural network (CNN) model. The first model yields a training accuracy of 94.84% and validation accuracy of 81.25% for car parts classification. The second model yields a training accuracy of 97.16% and validation accuracy of 49.28% for damage identification.