A Weld Defect Recognition Method Based on Improved Lenet-5 Model

Abstract

The invention discloses a weld defect recognition method based on the improved LeNet-5 model. Firstly, for the weld gray-scale image, the input of the traditional convolution kernel channel of the LeNet-5 model is improved, and the gray-scale image is passed through pseudo-color The enhancement technology is converted into a color image, and the obtained color image is used as the input of the neural network; then the convolution kernel of the LeNet-5 model is improved, and the convolution kernel channel with the Gabor filter is added; in the sixth layer of the neural network, The features obtained from multiple channels are fused to obtain the feature set T; finally, the SoftMax classifier is used in the seventh layer (output layer) of the neural network to obtain the defect type of the weld and the probability of each category, which is used for evaluation. It provides a reference for film personnel to determine the type of film and formulate on-site repair plans. The invention improves the feature extraction ability of the neural network, thereby improving the correct rate of defect recognition; the recognition result is given by the probability that the defect belongs to a certain category, and provides more sufficient reference information for reviewers.

Description

technical field [0001] The invention belongs to the technical field of weld defect recognition, and in particular relates to a weld defect recognition method based on an improved LeNet-5 model. Background technique [0002] In the field of automatic identification of weld defects, traditional methods inevitably go through the process of manually selecting features, which is time-consuming and labor-intensive, and whether the selection of features is reasonable is highly subjective, which has a great impact on the accuracy of identification. [0003] The weld seam image is a grayscale image, and the grayscale image is directly input into the neural network, and there is a problem that the original feature representation is not sufficient. [0004] Existing convolutional neural networks often rely on a single type of convolution kernel for the convolution process, which can easily lead to insufficient feature extraction, thereby affecting the accuracy of defect recognition. ...

AI Technical Summary

Problems solved by technology

[0006] The technical problem to be solved by the present invention is to provide a method for identifying weld defects based on the improved LeNet-5 model for the deficiencies in the above-mentioned prior art, which avoids the process of manually selecting features in traditional methods; The amount of information input by the neural network is reduced. By adding the convolution channel of the Gabor filter, the improved neural network has both the traditional convolution kernel channel and the Gabor filter channel, which improves the feature extraction ability of the neural network, thereby improving the accuracy of defect identification. Correct rate; the recognition result is given by the probability that the defect belongs to a certain category, which provides more sufficient reference information for reviewers

Images