Automated Detection of Multi-class Vehicle Exterior Damages using Deep Learning
Maleika Heenaye-Mamode Khan, Mohammad Zafir Hussein Sk Heerah, Zuhairah Basgeeth
Abstract
In this paper, we have deployed an application for the automatic detection and classification of vehicle damages to be used by the insurance companies for processing claims or by the police department for the recording of accidents. It is often tedious to manually identify the types and severity of damages on vehicles occurred after an accident. Automated damage detection application can ease the insurance claim process. Convolution Neural Networks (CNN) have achieved great success in the classifications of objects. However, CNN has not been fully explored and applied for the multiclass classifications of vehicle damages. In this work, we have adapted the pre-trained CNN models namely the MobileNet and VGG19 and applied a transfer learning on our large constructed dataset. We have also investigated overfitting and have trained the model to learn more general features. Our application has achieved an accuracy of 70% for MobileNet and 50% for VGG19. Unlike existing models, our proposed model is able to detect and classify multiple types of damages namely dents, scratches, broken and not damage, which is an achievement in this problem domain.