Automatic damaged vehicle estimator using enhanced deep learning algorithm

Abstract

Claim leakage costs insurance companies millions of dollars each year because of the disparity between the cost spent by allowance businesses and the accurate quantity that must be reimbursed. As a result, processing claims for identifying and classifying automobile damages takes time and is costly for insurance providers. In this paper, we used an improved Mask R-CNN method, which has a significant research benefit of object detection, to automatically detect, identify, and categorize car damage sites in traffic incidents. To detect and label an image of a damaged vehicle, we used a combination of deep learning, transfer learning, Mask R-CNN, and instance segmentation. In addition, a web-based automatic claim estimator can accept photographs from the user and determine the position and degree of the damage automatically. Furthermore, three different pre-trained models, namely inception ResNetV2, VGG-16, and VGG-19, were used to aid quick convergence. Finally, comparative performance assessments employ several evaluation measures such as precision, recall, F1 score, accuracy, loss function, and confusion matrices based on the three pre-trained models. The empirical results reveal that the proposed method not only recognizes damaged vehicles but also locates them and determines their severity level which accomplishes the study's objective of automatically locating and classifying car damage. According to the data, employing Mask-RCNN with pre-trained Inception ResNetV2 outperforms the other models in all detection, localization, and severity-damaged performance categories.

Keywords

• Vehicle damage assessment
• Deep learning
• Instance segmentation, Mask R-CNN, Object detection, Transfer learning