Method and device for evaluating vehicle loss recognition model

Abstract

The embodiment of the invention provides a method and a device for evaluating a vehicle loss recognition model, and the method comprises the steps: firstly, obtaining a test sample, a vehicle loss picture corresponding to the test sample, and a plurality of groups of annotation data, and the plurality of groups of annotation data being generated by annotating the vehicle loss picture at least based on a plurality of annotation personnel; then determining an intersection and a union of multiple groups of labeled data, and determining a significant damage object set in the test sample accordingto the intersection; and determining a non-significant damage object set in the test sample according to the difference between the intersection and the union. In addition, the vehicle damage pictureis input into a pre-trained vehicle damage recognition model, and a predicted damage object set output by the model for the test sample is obtained. Therefore, the test result of the vehicle damage identification model on the test sample can be determined according to the relationship between the predicted damage object set and the significant damage object set and the relationship between the predicted damage object set and the non-significant damage object set.

Description

technical field [0001] One or more embodiments of this specification relate to the field of machine learning, and in particular to a method and device for evaluating a vehicle damage recognition model. Background technique [0002] In the process of traditional auto insurance claims, the insurance company needs to send professional survey and loss assessment personnel to the accident scene to conduct on-site survey and assessment of damage, provide the vehicle maintenance plan and compensation amount, and take photos of the scene, and keep the damage assessment photos for background verification Personnel check damage and price. Due to the need for manual damage assessment, insurance companies need to invest a lot of labor costs and professional knowledge training costs. From the experience of ordinary users, the claim settlement process takes as long as 1-3 days due to waiting for the manual surveyor to take pictures on site, the damage assessor to determine the damage at ...

AI Technical Summary

Problems solved by technology

Due to the need for manual survey and loss assessment, insurance companies need to invest a lot of labor costs and professional knowledge training costs

From the experience of ordinary users, the claim settlement process is as long as 1-3 days due to waiting for the manual surveyor to take pictures on the spot,

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