Tire code and tire state identification method based on machine learning

A machine learning and state recognition technology, applied in the field of automatic processing of tire pictures to identify tire codes and tire states, can solve the problems of poor timeliness, time-consuming and laborious, etc., and achieve the effect of shortening time, reducing labor costs and high recognition accuracy

Pending Publication Date: 2019-07-26
南京链和科技有限公司
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Problems solved by technology

[0004] In view of the time-consuming and labor-intensive and poor timeliness of manual inspection of tire conditions at present, the present invention proposes a tire code and tire status recognition method based on machine learning, constructs a multi-layer neural networ...

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  • Tire code and tire state identification method based on machine learning
  • Tire code and tire state identification method based on machine learning
  • Tire code and tire state identification method based on machine learning

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Embodiment Construction

[0041] Below in conjunction with accompanying drawing, technical scheme of the present invention will be further described:

[0042] A tire code and tire status recognition method based on machine learning, such as figure 1 shown, including the following steps:

[0043] S1. Collect the tire image, and preprocess the image, and normalize the pixel value of each pixel in the image to [0, 1].

[0044] S2, using the pixel attributes of the tire image as the input neuron signal to establish a single-layer neural network model, and calculate the model output function; the specific operations are as follows:

[0045] S21. Establish a single-layer neural network model. Assuming that there are n pixels in a tire image, the attributes of each pixel are: 2-dimensional pixel size, 1-dimensional gray value, RGB 3-dimensional color size, and these 6-dimensional data are unified as the input of the single-layer neural network model Signal, in the neural network model, one pixel correspond...

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Abstract

The invention provides a tire code and tire state identification method based on machine learning, comprising the following steps: S1, acquiring a tire image, and preprocessing the image; S2, establishing a single-layer neural network model, and calculating a model output function; S3, based on an error back propagation algorithm, iteratively updating S2, and establishing a multi-layer neural network model which comprises an input layer, a hidden layer and an output layer; S4, establishing a convolutional neural network as a hidden layer in the multi-layer neural network; S5, establishing a full connection layer to connect the hidden layer with the output layer, and outputting the tire code and the tire state of the tire by the output layer; S6, carrying out supervising learning on the multilayer neural network through the artificially marked verification set, and updating neural network parameters; and S7, processing the tire image by using the trained multi-layer neural network to obtain a corresponding tire code and a tire state. According to the method, the tire inspection time is effectively shortened, the identification precision is high, and convenience is provided for automobile safety inspection, accurate tire maintenance, tire leasing service and the like.

Description

technical field [0001] The invention relates to a method for automatically processing tire pictures to identify tire codes and tire states, and belongs to the technical field of automobile safety. Background technique [0002] As a major part of a car, car tires consume a lot in daily travel, and high-quality testing methods are required to provide guarantees in terms of use time, mileage, energy consumption, and use safety. [0003] At present, the use status and safety inspection of automobile tires are mainly carried out by manpower, requiring maintenance personnel to check under the vehicle or disassemble the tires for inspection, which is not only time-consuming and labor-intensive, but also has different accuracy rates for workers with different experience. In addition, manual inspection is difficult to accurately determine the degree of tire wear, remaining service life, etc., and it is impossible to detect potential safety problems in time, temporary emergencies such...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06N3/045G06F18/217
Inventor 向卫
Owner 南京链和科技有限公司
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