A new method using the convolutional neural network with compressive sensing for fabric defect classification based on small sample sizes

2018 ◽  
Vol 89 (17) ◽  
pp. 3539-3555 ◽  
Author(s):  
Bing Wei ◽  
Kuangrong Hao ◽  
Xue-song Tang ◽  
Yongsheng Ding
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Compressive Sensing ◽  
Classification Accuracy ◽  
Small Sample ◽  
Compressive Sampling ◽  
New Method ◽  
Sample Sizes ◽  
Fabric Defect ◽  
Small Sample Sizes

The convolutional neural network (CNN) has recently achieved great breakthroughs in many computer vision tasks. However, its application in fabric texture defects classification has not been thoroughly researched. To this end, this paper carries out a research on its application based on the CNN model. Meanwhile, since the CNN cannot achieve good classification accuracy in small sample sizes, a new method combining compressive sensing and the convolutional neural network (CS-CNN) is proposed. Specifically, this paper uses the compressive sampling theorem to compress and augment the data in small sample sizes; then the CNN can be employed to classify the data features directly from compressive sampling; finally, we use the test data to verify the classification performance of the method. The explanatory experimental results demonstrate that, in comparison with the state-of-the-art methods for running time, our CS-CNN approach can effectively improve the classification accuracy in fabric defect samples, even with a small number of defect samples.

scholarly journals Side Scan Sonar Shipwreck Image Recognition Algorithm based on Transfer Learning and Deep Learning

2021 ◽  
Vol 290 ◽  
pp. 02020
Author(s):  
Boyu Zhang ◽  
Xiao Wang ◽  
Shudong Li ◽  
Jinghua Yang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Image Recognition ◽  
Correlated Data ◽  
Small Sample ◽  
Side Scan Sonar ◽  
Transfer Training ◽  
Small Sample Sizes

Current underwater shipwreck side scan sonar samples are few and difficult to label. With small sample sizes, their image recognition accuracy with a convolutional neural network model is low. In this study, we proposed an image recognition method for shipwreck side scan sonar that combines transfer learning with deep learning. In the non-transfer learning, shipwreck sonar sample data were used to train the network, and the results were saved as the control group. The weakly correlated data were applied to train the network, then the network parameters were transferred to the new network, and then the shipwreck sonar data was used for training. These steps were repeated using strongly correlated data. Experiments were carried out on Lenet-5, AlexNet, GoogLeNet, ResNet and VGG networks. Without transfer learning, the highest accuracy was obtained on the ResNet network (86.27%). Using weakly correlated data for transfer training, the highest accuracy was on the VGG network (92.16%). Using strongly correlated data for transfer training, the highest accuracy was also on the VGG network (98.04%). In all network architectures, transfer learning improved the correct recognition rate of convolutional neural network models. Experiments show that transfer learning combined with deep learning improves the accuracy and generalization of the convolutional neural network in the case of small sample sizes.

scholarly journals Few-shot cotton leaf spots disease classification based on metric learning

2021 ◽  
Author(s):  
Xihuizi Liang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Leaf Spot ◽  
Classification Accuracy ◽  
Metric Learning ◽  
Small Sample ◽  
Cotton Leaf ◽  
Threshold Segmentation ◽  
Leaf Disease ◽  
Leaf Spots

Abstract Background: Cotton diceases seriously affect the yield and quality of cotton. The type of pest or disease suffered by cotton can be determined by the disease spots on the cotton leaves. This paper presents a small-sample learning framework that can be used for cotton leaf disease spot classification task, which using deep learning techniques is constructed based on a metric learning approach, to prevent and control cotton diseases timely. First, disease spots on cotton leaf's disease images are segmented by different methods, compared by using support vector machine (SVM) method and threshold segmentation, and discussed the suitable one. With segmented disease spot images as input, a disease spot dataset is established, and the cotton leaf disease spots were classified using a classical convolutional neural network classifier, the structure and framework of convolutional neural network had been designed, and the setting of relevant parameters and the detailed network structure configuration are analyzed according to the experimental environment. The features of two different images are extracted by a parallel two-way convolutional neural network with weight sharing. Then, the network uses a loss function to learn the metric space, in which similar leaf samples are close to each other and different leaf samples are far away from each other. Results: To achieve the classification of cotton leaf spots by small sample learning, this paper constructs a metric-based learning method to extract cotton leaf spot features and classify the leaves. In the process of leaf spot extraction, image segmentation of the spots is performed by threshold segmentation and SVM, and comparative analysis is performed. In the process of leaf spot classification, the structural framework of leaf spot feature extractor and feature classifier is constructed, and the overall framework is built using the idea of two-way parallel convolutional neural network. A variety of excellent convolutional neural network feature extractors such as Vgg, DesenNet, and ResNet were used for feature extraction work, and a combination design based on the small sample classification framework was performed and compared. Experimentally, it is demonstrated that the classification accuracy is improved by nearly 7.7% on average for different number of samples in the case of using this optimizer. S-DesneNet have the highest accuracy. When n is 5, 10, 15 and 20, the accuracy is 58.63%, 84.41% ,92.51% and 91.75%, respectively, and the average accuracy is improved by nearly 7.7% compared with DenseNet. Conclusions: To solve the problem of classification accuracy degradation due to small number of samples in small sample training tasks, a spatial structure optimizer (SSO) acting on the training process is proposed for this purpose.

scholarly journals Deep Nearest Neighbor Website Fingerprinting Attack Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Maohua Guo ◽  
Jinlong Fei ◽  
Yitong Meng
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Research Methods ◽  
Classification Accuracy ◽  
Nearest Neighbor ◽  
Concept Drift ◽  
Small Sample ◽  
K Nearest Neighbor ◽  
Encrypted Traffic ◽  
Fingerprinting Attack

By website fingerprinting (WF) technologies, local listeners are enabled to track the specific website visited by users through an investigation of the encrypted traffic between the users and the Tor network entry node. The current triplet fingerprinting (TF) technique proved the possibility of small sample WF attacks. Previous research methods only concentrate on extracting the overall features of website traffic while ignoring the importance of website local fingerprinting characteristics for small sample WF attacks. Thus, in the present paper, a deep nearest neighbor website fingerprinting (DNNF) attack technology is proposed. The deep local fingerprinting features of websites are extracted via the convolutional neural network (CNN), and then the k-nearest neighbor (k-NN) classifier is utilized to classify the prediction. When the website provides only 20 samples, the accuracy can reach 96.2%. We also found that the DNNF method acts well compared to the traditional methods in coping with transfer learning and concept drift problems. In comparison to the TF method, the classification accuracy of the proposed method is improved by 2%–5% and it is only dropped by 3% when classifying the data collected from the same website after two months. These experiments revealed that the DNNF is a more flexible, efficient, and robust website fingerprinting attack technology, and the local fingerprinting features of websites are particularly important for small sample WF attacks.

scholarly journals Herbal plant recognition using deep convolutional neural network

2020 ◽  
Vol 9 (5) ◽  
pp. 2198-2205
Author(s):  
Izwan Asraf Md Zin ◽  
Zaidah Ibrahim ◽  
Dino Isa ◽  
Sharifah Aliman ◽  
Nurbaity Sabri ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Data Augmentation ◽  
Small Sample ◽  
Deep Convolutional Neural Network ◽  
Multiple Parameters ◽  
Plant Recognition ◽  
Herbal Plants ◽  
Small Sample Sizes ◽  
Herbal Plant

This paper investigates the application of deep Convolutional Neural Network (CNN) for herbal plant recognition through leaf identification. Traditional plant identification is often time-consuming due to varieties as well as similarities possessed within the plant species. This study shows that a deep CNN model can be created and enhanced using multiple parameters to boost recognition accuracy performance. This study also shows the significant effects of the multi-layer model on small sample sizes to achieve reasonable performance. Furthermore, data augmentation provides more significant benefits on the overall performance. Simple augmentations such as resize, flip and rotate will increase accuracy significantly by creating invariance and preventing the model from learning irrelevant features. A new dataset of the leaves of various herbal plants found in Malaysia has been constructed and the experimental results achieved 99% accuracy

No Evidence that Experiencing Physical Warmth Promotes Interpersonal Warmth: Two Failures to Replicate Williams and Bargh (2008)

2018 ◽  
Author(s):  
Christopher Chabris ◽  
Patrick Ryan Heck ◽  
Jaclyn Mandart ◽  
Daniel Jacob Benjamin ◽  
Daniel J. Simons
Keyword(s):  
Null Hypothesis ◽  
Small Sample ◽  
Sample Sizes ◽  
Double Blind ◽  
Bayesian Analyses ◽  
Physical Warmth ◽  
Small Sample Sizes ◽  
Interpersonal Warmth

Williams and Bargh (2008) reported that holding a hot cup of coffee caused participants to judge a person’s personality as warmer, and that holding a therapeutic heat pad caused participants to choose rewards for other people rather than for themselves. These experiments featured large effects (r = .28 and .31), small sample sizes (41 and 53 participants), and barely statistically significant results. We attempted to replicate both experiments in field settings with more than triple the sample sizes (128 and 177) and double-blind procedures, but found near-zero effects (r = –.03 and .02). In both cases, Bayesian analyses suggest there is substantially more evidence for the null hypothesis of no effect than for the original physical warmth priming hypothesis.

A visual terrain classification method for mobile robots’ navigation based on convolutional neural network and support vector machine

2021 ◽  
pp. 014233122098791
Author(s):  
Wanli Wang ◽  
Botao Zhang ◽  
Kaiqi Wu ◽  
Sergey A Chepinskiy ◽  
Anton A Zhilenkov ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Mobile Robots ◽  
Convolutional Neural Network ◽  
Hybrid Method ◽  
Classification Accuracy ◽  
Support Vector ◽  
High Classification Accuracy ◽  
Enhance Efficiency ◽  
Multi Class Classification

In this paper, a hybrid method based on deep learning is proposed to visually classify terrains encountered by mobile robots. Considering the limited computing resource on mobile robots and the requirement for high classification accuracy, the proposed hybrid method combines a convolutional neural network with a support vector machine to keep a high classification accuracy while improve work efficiency. The key idea is that the convolutional neural network is used to finish a multi-class classification and simultaneously the support vector machine is used to make a two-class classification. The two-class classification performed by the support vector machine is aimed at one kind of terrain that users are mostly concerned with. Results of the two classifications will be consolidated to get the final classification result. The convolutional neural network used in this method is modified for the on-board usage of mobile robots. In order to enhance efficiency, the convolutional neural network has a simple architecture. The convolutional neural network and the support vector machine are trained and tested by using RGB images of six kinds of common terrains. Experimental results demonstrate that this method can help robots classify terrains accurately and efficiently. Therefore, the proposed method has a significant potential for being applied to the on-board usage of mobile robots.

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