Texture image surface defect detection method based on depth convolution auto-encoder

Abstract

The invention discloses a texture image surface defect detection method based on a depth convolution auto-encoder. The method includes sampling the images and obtaining image blocks to form a trainingset and a verification set, wherein the training set trains an auto-encoder; inputting the image blocks of the verification set into an auto-encoder for processing to obtain a segmentation thresholdreference value; sequentially sampling the images to be detected to obtain image blocks, and inputting the image blocks into the auto-encoder to obtain reconstructed image blocks and feature vectors of the input and reconstructed image blocks; splicing and differentiating the reconstructed image blocks to obtain an initial segmentation image, performing similarity processing on the feature vectorsof each input image block and the corresponding reconstructed image block, splicing and interpolating to obtain an auxiliary segmentation image, multiplying the initial segmentation image and the auxiliary segmentation image element by element, and thresholding to obtain a binary segmentation image. According to the method, the texture surface defect detection model with high universality and robustness is obtained through training under a small number of normal samples, and the defect recognition precision is improved.

Description

technical field [0001] The invention relates to a surface image processing detection method in the technical fields of computer vision and industrial automation, in particular to a texture image surface defect detection method based on a deep convolutional self-encoder. Background technique [0002] Texture surface defect detection is a quality control technology that recognizes the position and shape of defects through product surface texture images. It has a large number of applications in production practice, such as textile inspection, wood inspection, etc. The traditional texture surface defect detection is mainly based on manual visual inspection, which is completed by experienced technicians, which makes the subjective factors of the inspectors involved in the inspection work, often manifested as unstable inspection quality, and manual inspection methods often fail to achieve for the purpose of real-time detection. In recent years, the detection of texture surface de...

AI Technical Summary

Problems solved by technology

[0005] In order to solve the shortcomings of traditional texture analysis methods such as low versatility and robustness and the current deep learning-based texture surface defect detection method relying on a large number of labeled samples, the present invention proposes a texture image surface defect detection method based on a deep convolutional autoencoder. Detection methods to address the above-mentioned deficiencies of existing detection methods

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