License plate classification method and system based on deep learning

Abstract

The invention discloses a license plate classification method and system based on deep learning, and is applied to an electronic apparatus. The method comprises the following steps: obtaining at least one picture sent by a terminal device; preprocessing the obtained at least one picture so as to enable the preprocessed at least one picture to match input parameters of a pre-trained identification model; and inputting the preprocessed at least one picture into the pre-trained identification model, identifying vehicle purpose information corresponding to the at least one picture by use of the pre-trained identification model, and sending the vehicle purpose information corresponding to the at least one picture to the terminal device. The purpose of a vehicle in the picture can be accurately and automatically identified, accurate classification can also be realized, and the accuracy and the work efficiency are improved.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a deep learning-based license plate classification method and system. Background technique [0002] At present, in the relevant auto insurance business (for example, auto insurance claims business), insurance companies will obtain a large number of claims vehicle photos. In order to effectively manage claim vehicle photos, insurance companies usually use manual methods to determine the relevant vehicles in each claim vehicle photo. According to the purpose of the vehicle (for example, truck use, police use, private car use, military use, etc.), and then manually classify the claims vehicle photos according to the purpose of the vehicle, this method will not only consume a lot of manpower and material resources, but also error-prone. Contents of the invention [0003] In view of the above, it is necessary to provide a license plate classification method and system based ...

AI Technical Summary

Problems solved by technology

[0002] At present, in the relevant auto insurance business (for example, auto insurance claims business), insurance companies will obtain a large number of claims vehicle photos. In order to effectively manage claim vehicle photos, insurance companies usually use manual methods to determine the relevant vehicles in each claim vehicle photo. According to the purpose of the vehicle (for example, truck use, police use, private car use, military use, etc.), and then manually classify the photos of the claimed vehicles according to the purpose of the vehicle, this method will not only consume a lot of manpower and material resources, but also error-prone

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