Cloth defect intelligent identification method based on deep learning

Abstract

The invention discloses a cloth defect intelligent recognition method based on deep learning, and the method comprises the following steps: S1, carrying out the photographing of cloth with different forms of defects through a camera, obtaining a picture material, and dividing the picture material into a training set and a verification set according to a certain proportion; S2, labeling the obtained picture material; S3, inputting the labeled data into an object detection model for training, and storing model parameters obtained by training; and S4, endowing the obtained model parameters to anoriginal model, taking a test image as the input of the model, processing the input image test, carrying out feature processing on the test image by the SSD model, and then predicting the type and theposition of the defect. According to the method, the end-to-end processing effect is achieved, the working efficiency is improved, and meanwhile, the robustness of the model is effectively improved through complex feature extraction calculation in the model.

Description

technical field [0001] The invention relates to the field of computer vision and image processing of deep learning, in particular to a method for intelligent recognition of cloth defects based on deep learning. Background technique [0002] Cloth defect inspection is an important link in the production and quality management of the textile industry. The current manual inspection is slow, labor-intensive, affected by subjective factors, and lacks consistency. In 2016, my country's cloth output exceeded 70 billion meters, and the output has been on the rise. If artificial intelligence and computer vision technology can be applied to the textile industry, the value to the textile industry will undoubtedly be huge. [0003] Deep learning is a major breakthrough in the field of machine learning in recent years. It has enabled computers to make significant progress in speech, image, and semantic understanding, and has been widely used in many fields. This project applies deep lea...

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Problems solved by technology

[0004] The paper of the Institute of Artificial Intelligence of Huazhong University of Science and Technology, the application of deep learning in the detection of cloth defects (Zhao Zhiyong, Ye Lin et al. The application of deep learning in the detection of cloth defects [J]. Foreign Electronic Measurement Technology, 2019, 08: 110-116) using deep neural network for cloth defect detection, the success rate is high, but the detection speed is very slow

Liu Shanliang, Liu Fengzhou and others from Jiangsu University published a paper on neural network-based fabric image defect detection and positioning algorithm research (Liu Shanliang, Liu Fengzhou et al. Research on neural network fabric image defect detection and positioning algorithm [D]. Jiangsu University 2019 ) proposes to use the original SSD model to realize fabric defect detection, but the detection speed cannot meet the requirements of the industry

Huang Lei, Han Xiaojun and others from Tianjin University of Technology used mathematical morphology in their research on image information detection methods for fabric defects (Huang Lei, Han Xiaojun et al. Research on image information detection methods for fabric defects [D]. Tianjin University of Technology 2015). The method is used to detect fabric defects, but the accuracy and speed need to be improved

Among the current cloth detection solutions, there is a model with too much calculation, and the detection speed and detection accuracy cannot meet the industrial requirements at the same time.

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