A visual long-short-term memory based integrated CNN model for fabric defect image classification

Zhao, Yudi; Hao, Kuangrong; He, Haibo; Tang, Xue-song; Wei, Bing

doi:10.1016/j.neucom.2019.10.067

Cited by 66 publications

(44 citation statements)

References 26 publications

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“…Zhang M., Zhao Y., Sardogan M. et al proposed methods used in image classification based on CNN, and the classification accuracy was improved [24,25,26] . On this basis, Ge W. 1-(a).…”

Section: Related Workmentioning

confidence: 99%

Joint Face Retrieval System Based On a New Quadruplet Network in Videos of Multi-Camera

Ren

2021

IEEE Access

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At present, a large number of off-line videos is stored in the server of surveillance network. In order to retrieve the target face in these massive videos frames, the face retrieval system is designed. A new Quadruplet Network is constructed by changing the RELU structure of CNN network and training the new Quadruplet Network to acquire the depth features. Join with the online fugitive face picture that launched online to initiate the wanted, with the help of the depth feature contrast to launch the Content-Based Image Retrieval (CBIR). The new Quadruplet Network converges faster than familiar networks such as Alexnet, Googlenet, VGGNet and ResNet. Because of the shared weight design of the network, the retrieval has a high precision, recall and the retrieval rate. Image depth features can be shared quickly online between the cameras. The experimental results show that the proposed method is effective, with an accuracy of 98.74% and a precision of 99.54%, and a frame rate of 28 FPS.

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“…Zhang M., Zhao Y., Sardogan M. et al proposed methods used in image classification based on CNN, and the classification accuracy was improved [24,25,26] . On this basis, Ge W. 1-(a).…”

Section: Related Workmentioning

confidence: 99%

Joint Face Retrieval System Based On a New Quadruplet Network in Videos of Multi-Camera

Ren

2021

IEEE Access

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“…The raw images used for recent research were mainly from public datasets or collected by textile factories and laboratories. Some typical public defect detection datasets are TILDA dataset ( ), DAGM2007 dataset ( ), and Hong Kong patterned texture database ( ); and some self-built datasets are DHU-FD-500 [ 7 ], DHU-FD-1000 [ 7 ], lattice [ 8 ], FDBF dataset [ 19 ], etc. Image preprocessing.…”

Section: Related Workmentioning

confidence: 99%

“…These traditional defect detection methods can get good results on some specific fabric products (unpatterned or regular patterned background). However, most of them are mainly based on predefined features or hand-craft features [7], including statistical features, structural features, and spectral features of the images. This means that the configuration of model parameters requires some prior knowledge or problem-specific research.…”

Section: Introductionmentioning

confidence: 99%

“…With this powerful feature learning capability, deep learning models based on CNN have been widely applied to various sub-fields of defect detection, such as fabric defect detection, lithium battery defect detection. For example, Zhao et al [7] proposed a fabric defect classification method using a three-parallel-module integrated structure (i.e., Stacked Convolutional Auto-Encoders (SCAE), a shallow CNN and a deep CNN combining non-local block). Gan et al [9] designed a joint detection CNN architecture, which contains two major parts: The global detection part (used for background classification in image level) and the sub detection part (used for defect classification in image patch level).…”

Section: Introductionmentioning

confidence: 99%

“…Some typical public defect detection datasets are TILDA dataset (https://lmb.informatik.uni-freiburg.de/resources/datasets/ tilda.en.html), DAGM2007 dataset (https://hci.iwr.uni-heidelberg.de/content/weakly-supervisedlearning-industrial-optical-inspection), and Hong Kong patterned texture database (https://ytngan. wordpress.com/codes/); and some self-built datasets are DHU-FD-500 [7], DHU-FD-1000 [7], lattice [8], FDBF dataset [19], etc.…”

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confidence: 99%

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A Robust Fabric Defect Detection Method Based on Improved RefineDet

Xie

2020

Sensors

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This paper proposes a robust fabric defect detection method, based on the improved RefineDet. This is done using the strong object localization ability and good generalization of the object detection model. Firstly, the method uses RefineDet as the base model, inheriting the advantages of the two-stage and one-stage detectors and can efficiently and quickly detect defect objects. Secondly, we design an improved head structure based on the Full Convolutional Channel Attention (FCCA) block and the Bottom-up Path Augmentation Transfer Connection Block (BA-TCB), which can improve the defect localization accuracy of the method. Finally, the proposed method applies many general optimization methods, such as attention mechanism, DIoU-NMS, and cosine annealing scheduler, and verifies the effectiveness of these optimization methods in the fabric defect localization task. Experimental results show that the proposed method is suitable for the defect detection of fabric images with unpattern background, regular patterns, and irregular patterns.

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A Fire Detection System Based on YOLOv4

Fei

Qian

2022

Advances in Artificial Intelligence and Security

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A visual long-short-term memory based integrated CNN model for fabric defect image classification

Cited by 66 publications

References 26 publications

Joint Face Retrieval System Based On a New Quadruplet Network in Videos of Multi-Camera

Joint Face Retrieval System Based On a New Quadruplet Network in Videos of Multi-Camera

A Robust Fabric Defect Detection Method Based on Improved RefineDet

A Fire Detection System Based on YOLOv4

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