Deep Learning Based Car Damage Detection, Classification and Severity

In the accident insurance industry, settling the claim is a time-consuming process since it is a manual process and there is a gap between the optimal and the actual settlement. Using deep learning models, we are not only trying to speed up the process but also provide better customer service and increase the profitability of insurance companies. In this paper we are using various pretrained models such as VGG 16, VGG 19, Resnet50 and Densenet and based on these models, selecting the best performing models. We initially check whether the car is damaged or not using the Resnet50 model and if it's a damaged one we use the WPOD-net model to detect the license plate. To identify the damaged region, we use the YOLO model. At last, comes the damage severity which is implemented using the Densenet model. After implementing various models, we find out that transfer learning gives better results than fine-tuning. In addition to that we propose a framework that integrates all of this into one application and in turn helps in the automation of the insurance industry.