Damage detection model training method and device, vehicle damage detection method and device, equipment and medium

Abstract

The invention relates to artificial intelligence, and provides a damage detection model training method and device, a vehicle damage detection method and device, equipment and a medium. The method comprises the steps: inputting an obtained damage sample image into a damage detection model containing a first parameter, extracting damage features, and generating an intermediate convolution feature map; inputting the intermediate convolution feature map into a mask prediction branch model containing a second parameter; outputting a training result through the damage detection model according to the damage features, and obtaining a mask result through the mask prediction branch model; obtaining a first loss value through the first loss model, and obtaining a second loss value through the second loss model; obtaining a total loss value; and when the total loss value does not reach the convergence condition, iteratively updating the first parameter and the second parameter until the total loss value reaches the convergence condition to obtain a trained damage detection model. According to the invention, the damage type and the damage area can be quickly identified. The invention also relates to a blockchain technology. The damaged sample image can be stored in the blockchain.

Description

technical field [0001] The invention relates to the field of artificial intelligence classification models, in particular to a damage detection model training, a vehicle damage detection method, a device, a computer device and a storage medium. Background technique [0002] After a vehicle accident occurs, some parts of the vehicle will leave traces of damage such as damage, scratches, etc. At present, insurance companies generally manually identify images of vehicle damage after traffic accidents taken by vehicle owners or business personnel, that is, to The damage type and damage area of ​​the damaged part of the vehicle in the image are manually identified and judged. In this way, due to the influence of different understanding of standards and insufficient observation experience, the artificially identified damage type and damage area may not match; for example: due to depression and Scratches are difficult to distinguish through visual inspection images, and damage asse...

AI Technical Summary

Problems solved by technology

[0002] After a vehicle accident occurs, some parts of the vehicle will leave traces of damage such as damage, scratches, etc. At present, insurance companies generally manually identify images of vehicle damage after traffic accidents taken by vehicle owners or business personnel, that is, to The damage type and damage area of ​​the damaged part of the vehicle in the image are manually identified and judged. In this way, due to the influence of different understanding of standards and insufficient observation experience, the artificially identified damage type and damage area may not match; for example: due to depression and Scratches are difficult to distinguish through visual inspection images, and damage assessment personnel can easily determine the type of damage that is sunken as the type of damage caused by scratches. The error in determining the loss caused by the above situation will greatly reduce the accuracy of the damage assessment; While causing the cost loss of the insurance company, it will also reduce the satisfaction of car owners or customers; in addition, the workload of manual loss determination is huge, and the efficiency of loss determination is low. When a certain accuracy of loss determination needs to be met, it will be further improved. workload, reduce work efficiency

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