Research on Vehicle Appearance Damage Recognition Based on Deep Learning

Abstract

Economic development has promoted the booming of the auto industry. With the increase of the number of cars, car insurance has become the largest type of insurance in the insurance industry with more than half of the market share. After the emergence of traditional vehicles, professional loss assessment personnel need to go to the scene to investigate the accident and complete the loss assessment. In recent years, With the rapid development of science and technology, the insurance industry has been changing from artificial and information to automation and intelligence. This paper presents a vehicle appearance damage recognition algorithm based on deep learning and its model evaluation method, which can accurately judge the vehicle damage in the image. The research shows that the Mask R-CNN model based on KL-loss performs well in vehicle damage detection and has good robustness; at the same time, the accuracy of the evaluation model results is greatly improved by replacing the traditional IOU calculation accuracy method with the component position.