Tire X-ray defect/flaw detection method based on deep convolutional neural network

Abstract

The invention belongs to the technical field of image recognition and detection and specifically discloses a tire X-ray defect / flaw detection method based on the deep convolutional neural network. The detection method comprises the steps of image preprocessing, data cleaning, convolutional neural network designing and training, parameter adjusting, input image preprocessing, model training and model testing. According to the invention, tire x-ray images are classified based on the convolutional neural network. Compared with the traditional method, image features can be automatically learned during the training process of the convolutional neural network, so that the defect of manually selecting features can be avoided. The occurrence rate of accidental errors is reduced. The detection result of the method is high in reliability and tire defect / flaw can be accurately detected. The outstanding feature of applying the convolutional neural network to the image classification is that, a lot of time needs to be consumed during the training process of the model, while only a short period of time is required for the usage of the convolutional neural network model. Therefore, the cost is saved. The method is convenient for wide popularization.

Description

technical field [0001] The invention belongs to the technical field of image recognition and detection, and in particular relates to a tire X-ray defect detection method based on a deep convolutional neural network. Background technique [0002] Today's deep learning methods based on big data have far surpassed traditional recognition and detection methods. Convolutional Neural Network (CNN for short) is one of the more popular methods of deep learning at present, and it is a feedforward neural network. , its artificial neurons can respond to surrounding units within a part of the coverage area, and it has excellent performance for large image processing. Deep learning is a branch of machine learning that attempts to perform high-level abstraction of data using algorithms that contain complex structures or multiple processing layers composed of multiple nonlinear transformations. [0003] The convolutional neural network gradually extracts the high-level features of the ima...

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to overcome the shortcomings of the existing tire X-ray defect detection method, and provide a tire X-ray defect detection method based on a deep convolutional neural network to solve the existing traditional tire X-ray defect detection method. Strong prior knowledge is required to assist in the extraction of discriminative features, which has the problems of strong contingency, low reliability, and inability to accurately detect tire defects

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