Vehicle loss detection method and device, electronic equipment and storage medium

Abstract

The invention discloses a vehicle loss detection method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining a target image; inputting the target image into a network model, a backbone network of the network model including a Swin Transformer network, the backbone network being used for predicting a damage position coordinate and a damage category of the target image based on the Swin Transformer network; and determining a damage detection result according to the damage position coordinate and the damage category. According to the embodiment of the invention, the Swin Transformer network is used as the backbone network, so that compared with a CNN detection mode, the method is more accurate, and can more effectively positions and identifies a damaged part. The Swin Transformer is used as the backbone network to extract features, so that spatial information relation among pixels of the image and weighted selection of the features can be explored, and better feature extraction and utilization are realized. In addition, the Swin Transformer has the characteristics of locality, translation invariance, residual learning and the like of the CNN, so that the problems of complicated calculation amount and large memory consumption in other visual Transformer schemes can be solved while the performance of the Swin Transformer method exceeds that of the CNN method.

Description

technical field [0001] Embodiments of the present invention relate to machine learning technology, and in particular to a vehicle loss detection method, device, electronic equipment, and storage medium. Background technique [0002] With the rapid development of society, vehicles have become one of the indispensable means of transportation, and the increasing number of vehicles undoubtedly increases the incidence of traffic accidents. After a traffic accident, the insurance company usually goes to the accident scene to determine the damage, that is, to determine the vehicle damage by observing the photos taken at the scene, and use it as the basis for the auto insurance company's claims. Because the link of loss determination consumes a lot of human resources, and the results obtained are highly subjective. Therefore, the vehicle damage detection system based on the deep learning method gradually replaces the manual operation, which can accurately detect the type of vehicle...

AI Technical Summary

Problems solved by technology

However, the process of image analysis based on CNN is not accurate enough

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