Fabric defect detection method based on deep separable convolutional neural network

Abstract

The invention provides a fabric defect detection method based on a deep separable convolutional neural network, and the method comprises the steps: firstly marking a collected fabric defect image through a marking tool as a fabric image data set, and dividing the fabric image data set into a training set and a test set; secondly, constructing a depth separable convolution module, and constructinga DefectNet network by using the depth separable convolution module; inputting the training set into a DefectNet network for training, and adjusting parameters of the DefectNet network by using a training strategy of asynchronous gradient descent to obtain a DefectNet network model; and finally, inputting the fabric image in the test set into a DefectNet network model to obtain a target defect anda position coordinate in the image, and framing a defect target in the image. According to the method, deep separable convolution and multi-scale feature extraction are combined to build the convolutional neural network model, the detection precision is very high, the detection speed is greatly improved, and the requirement of real-time detection is met.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a fabric defect detection method based on a deep separable convolutional neural network. Background technique [0002] In the process of textile production, various defects often occur, which seriously affect the product quality of textiles and bring great losses to enterprises. Fabric defect detection is the process of finding defects on the surface of the fabric, accurately locating the position of the defect, and judging the type of the defect. At present, manual detection of fabric defects is generally used, but manual detection of fabric defects is easily affected by subjective factors, resulting in low detection accuracy, low detection efficiency, and high cost. Therefore, the automatic detection technology of fabric defects based on computer vision has become a research hotspot. [0003] The current fabric defect detection algorithms are mainly divided into seve...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the technical problems of large amount of model calculation, slow detection speed, and low detection rate in the current convolutional neural network, the present invention proposes a fabric defect detection method based on a depth-separable convolutional neural network. The product structure constructs a high-speed convolution module to realize the convolution operation on the channel and the area separately, so that the network calculation amount is greatly reduced; and in the feature extraction part, a multi-scale feature extraction method is used to extract features from different down-sampled feature maps , to improve the detection ability of the model for small-sized defect targets

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