Fabric defect detection method based on depth neural network

Abstract

The invention discloses a fabric defect detection method based on a depth neural network. The method comprises the following steps: (1), an image acquisition system is built to acquire an image; (2), the image is segmented into experimental samples, fabric sample image data are enhanced at the same time, and a fabric image after enhancement serves as a training sample; (3), a depth neural network is designed; (4), parameters are set, the depth neural network is initialized, the training sample is fed to the depth neural network for training, and after network training is completed, the network model is saved; and (5), an inputted new fabric sample is fed to the network model for detection. According to the fabric defect detection method based on the depth neural network provided by the invention, with a convolutional neural network as a core, feature extraction is performed by a convolutional layer, a pooling layer retains effective features and reduces the amount of calculation, and a full connection layer is used for classification. A mini-batch gradient descent method is used for optimization, the generalization ability is enhanced through L2 regularization, defect recognition is carried out through determining the corresponding position of the maximum component outputted by a classifier, effects are shown in Figure 4, Actual presents the actual category of the sample, and Pred presents the predicted category of the sample.

Description

technical field [0001] The invention relates to a fabric defect detection method based on a deep neural network, which belongs to the technical field of image processing. Background technique [0002] In the prior art, cloth defect detection has become an important part of fabric product quality control, and defect detection methods play an important role in improving product quality, and fabric defect detection has become a hot research field. Traditional manual inspection methods have great limitations, such as high labor costs, difficulty in distinguishing small defects, long-term work will cause visual fatigue, and prone to false detection and missed detection. In order to improve product quality and reduce production cost, automatic detection of cloth defects has become an effective method to improve cloth quality. [0003] The key part of the automatic detection system for cloth defects is the method of defect detection. The common defect detection methods can be roug...

AI Technical Summary

Problems solved by technology

These methods can achieve good results only when identifying specific targets, and do not have good adaptability

Moreover, when the image background is complex and the defect is difficult to distinguish, the recognition rate will decrease, and the complexity of the method will also increase.

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