Vehicle damage feature detection method and device, computer equipment and storage medium

Abstract

The invention relates to the field of artificial intelligence, and discloses a vehicle damage feature detection method and device, computer equipment and a storage medium. The vehicle damage feature detection method comprises the steps of obtaining a to-be-detected vehicle damage image and inputting the to-be-detected vehicle damage image into an unsupervised domain adaptive network model; extracting vehicle features through a migration learning model based on a pytorch, and generating a local feature map and a global feature map; outputting a migration feature vector group according to the vehicle features, obtaining a first self-adaptive feature vector group through a strong local feature self-adaptive model, and obtaining a second self-adaptive feature vector group through a weak globalfeature self-adaptive model; and performing regularization processing on the migration feature vector group, the first adaptive feature vector group and the second adaptive feature vector group to obtain an identification result. According to the vehicle damage feature detection method, the damage type and the damage area in the to-be-detected vehicle damage image can be automatically identified.The invention also relates to a blockchain technology. The unsupervised domain adaptive network model in the invention can be stored in the blockchain.

Description

technical field [0001] The invention relates to the field of image classification of artificial intelligence, in particular to a vehicle damage feature detection method, device, computer equipment and 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 assessment personnel can easily ...

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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