scholarly journals Classification Accuracy Improvement for Small-Size Citrus Pests and Diseases Using Bridge Connections in Deep Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4992
Author(s):  
Shuli Xing ◽  
Malrey Lee
Keyword(s):  
Neural Networks ◽  
Classification Accuracy ◽  
Deep Neural Networks ◽  
Citrus Fruit ◽  
Target Object ◽  
Accuracy Improvement ◽  
Pests And Diseases ◽  
The Rich ◽  
New Feature ◽  
Bridge Connections

Due to the rich vitamin content in citrus fruit, citrus is an important crop around the world. However, the yield of these citrus crops is often reduced due to the damage of various pests and diseases. In order to mitigate these problems, several convolutional neural networks were applied to detect them. It is of note that the performance of these selected models degraded as the size of the target object in the image decreased. To adapt to scale changes, a new feature reuse method named bridge connection was developed. With the help of bridge connections, the accuracy of baseline networks was improved at little additional computation cost. The proposed BridgeNet-19 achieved the highest classification accuracy (95.47%), followed by the pre-trained VGG-19 (95.01%) and VGG-19 with bridge connections (94.73%). The use of bridge connections also strengthens the flexibility of sensors for image acquisition. It is unnecessary to pay more attention to adjusting the distance between a camera and pests and diseases.

scholarly journals Generative Enhancement of 3D Image Classifiers

2020 ◽  
Vol 10 (21) ◽  
pp. 7433
Author(s):  
Michal Varga ◽  
Ján Jadlovský ◽  
Slávka Jadlovská
Keyword(s):  
Neural Networks ◽  
Knowledge Sharing ◽  
Classification Accuracy ◽  
Network Architecture ◽  
Deep Neural Networks ◽  
3D Image ◽  
Accuracy Improvement ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Generative Modeling

In this paper, we propose a methodology for generative enhancement of existing 3D image classifiers. This methodology is based on combining the advantages of both non-generative classifiers and generative modeling. Its purpose is to streamline the synthesis of novel deep neural networks by embedding existing compatible classifiers into a generative network architecture. A demonstration of this process and evaluation of its effectiveness is performed using a 3D convolutional classifier and its generative equivalent—a 3D conditional generative adversarial network classifier. The results of the experiments show that the generative classifier delivers higher performance, gaining a relative classification accuracy improvement of 7.43%. An increase of accuracy is also observed when comparing it to a plain convolutional classifier that was trained on a dataset augmented with samples created by the trained generator. This suggests a desirable knowledge sharing mechanism exists within the hybrid discriminator-classifier network.

scholarly journals Privacy-Preserving Deep Neural Network Methods: Computational and Perceptual Methods—An Overview

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1367
Author(s):  
Raghida El El Saj ◽  
Ehsan Sedgh Sedgh Gooya ◽  
Ayman Alfalou ◽  
Mohamad Khalil
Keyword(s):  
Neural Networks ◽  
Classification Accuracy ◽  
Deep Neural Networks ◽  
Privacy Preserving ◽  
Sensitive Data ◽  
Encrypted Data ◽  
High Classification Accuracy ◽  
Hide Information ◽  
Network Methods ◽  
Cloud Environments

Privacy-preserving deep neural networks have become essential and have attracted the attention of many researchers due to the need to maintain the privacy and the confidentiality of personal and sensitive data. The importance of privacy-preserving networks has increased with the widespread use of neural networks as a service in unsecured cloud environments. Different methods have been proposed and developed to solve the privacy-preserving problem using deep neural networks on encrypted data. In this article, we reviewed some of the most relevant and well-known computational and perceptual image encryption methods. These methods as well as their results have been presented, compared, and the conditions of their use, the durability and robustness of some of them against attacks, have been discussed. Some of the mentioned methods have demonstrated an ability to hide information and make it difficult for adversaries to retrieve it while maintaining high classification accuracy. Based on the obtained results, it was suggested to develop and use some of the cited privacy-preserving methods in applications other than classification.

scholarly journals Impact of Low Resolution on Image Recognition with Deep Neural Networks: An Experimental Study

2018 ◽  
Vol 28 (4) ◽  
pp. 735-744 ◽  
Author(s):  
Michał Koziarski ◽  
Bogusław Cyganek
Keyword(s):  
Neural Networks ◽  
Image Recognition ◽  
Classification Accuracy ◽  
Deep Neural Networks ◽  
Dynamic Range ◽  
Super Resolution ◽  
Image Resolution ◽  
Quality Data ◽  
Low Resolution ◽  
The Impact

Abstract Due to the advances made in recent years, methods based on deep neural networks have been able to achieve a state-of-the-art performance in various computer vision problems. In some tasks, such as image recognition, neural-based approaches have even been able to surpass human performance. However, the benchmarks on which neural networks achieve these impressive results usually consist of fairly high quality data. On the other hand, in practical applications we are often faced with images of low quality, affected by factors such as low resolution, presence of noise or a small dynamic range. It is unclear how resilient deep neural networks are to the presence of such factors. In this paper we experimentally evaluate the impact of low resolution on the classification accuracy of several notable neural architectures of recent years. Furthermore, we examine the possibility of improving neural networks’ performance in the task of low resolution image recognition by applying super-resolution prior to classification. The results of our experiments indicate that contemporary neural architectures remain significantly affected by low image resolution. By applying super-resolution prior to classification we were able to alleviate this issue to a large extent as long as the resolution of the images did not decrease too severely. However, in the case of very low resolution images the classification accuracy remained considerably affected.

scholarly journals A New Image Classification Approach via Improved MobileNet Models with Local Receptive Field Expansion in Shallow Layers

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Wei Wang ◽  
Yiyang Hu ◽  
Ting Zou ◽  
Hongmei Liu ◽  
Jin Wang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Computational Complexity ◽  
Embedded Systems ◽  
Receptive Field ◽  
Image Classification ◽  
Classification Accuracy ◽  
Deep Neural Networks ◽  
Classification Approach ◽  
Dilated Convolution ◽  
Convolution Filters

Because deep neural networks (DNNs) are both memory-intensive and computation-intensive, they are difficult to apply to embedded systems with limited hardware resources. Therefore, DNN models need to be compressed and accelerated. By applying depthwise separable convolutions, MobileNet can decrease the number of parameters and computational complexity with less loss of classification precision. Based on MobileNet, 3 improved MobileNet models with local receptive field expansion in shallow layers, also called Dilated-MobileNet (Dilated Convolution MobileNet) models, are proposed, in which dilated convolutions are introduced into a specific convolutional layer of the MobileNet model. Without increasing the number of parameters, dilated convolutions are used to increase the receptive field of the convolution filters to obtain better classification accuracy. The experiments were performed on the Caltech-101, Caltech-256, and Tubingen animals with attribute datasets, respectively. The results show that Dilated-MobileNets can obtain up to 2% higher classification accuracy than MobileNet.

scholarly journals Prediction of Causative Genes in Inherited Retinal Disorders from Spectral-Domain Optical Coherence Tomography Utilizing Deep Learning Techniques

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Yu Fujinami-Yokokawa ◽  
Nikolas Pontikos ◽  
Lizhu Yang ◽  
Kazushige Tsunoda ◽  
Kazutoshi Yoshitake ◽  
...  
Keyword(s):  
Neural Networks ◽  
Optical Coherence Tomography ◽  
Classification Accuracy ◽  
Deep Neural Networks ◽  
Test Accuracy ◽  
Test Sets ◽  
The Mean ◽  
Per Gene ◽  
Sd Oct ◽  
Gene Category

Purpose. To illustrate a data-driven deep learning approach to predicting the gene responsible for the inherited retinal disorder (IRD) in macular dystrophy caused by ABCA4 and RP1L1 gene aberration in comparison with retinitis pigmentosa caused by EYS gene aberration and normal subjects. Methods. Seventy-five subjects with IRD or no ocular diseases have been ascertained from the database of Japan Eye Genetics Consortium; 10 ABCA4 retinopathy, 20 RP1L1 retinopathy, 28 EYS retinopathy, and 17 normal patients/subjects. Horizontal/vertical cross-sectional scans of optical coherence tomography (SD-OCT) at the central fovea were cropped/adjusted to a resolution of 400 pixels/inch with a size of 750 × 500 pix2 for learning. Subjects were randomly split following a 3 : 1 ratio into training and test sets. The commercially available learning tool, Medic mind was applied to this four-class classification program. The classification accuracy, sensitivity, and specificity were calculated during the learning process. This process was repeated four times with random assignment to training and test sets to control for selection bias. For each training/testing process, the classification accuracy was calculated per gene category. Results. A total of 178 images from 75 subjects were included in this study. The mean training accuracy was 98.5%, ranging from 90.6 to 100.0. The mean overall test accuracy was 90.9% (82.0–97.6). The mean test accuracy per gene category was 100% for ABCA4, 78.0% for RP1L1, 89.8% for EYS, and 93.4% for Normal. Test accuracy of RP1L1 and EYS was not high relative to the training accuracy which suggests overfitting. Conclusion. This study highlighted a novel application of deep neural networks in the prediction of the causative gene in IRD retinopathies from SD-OCT, with a high prediction accuracy. It is anticipated that deep neural networks will be integrated into general screening to support clinical/genetic diagnosis, as well as enrich the clinical education.

scholarly journals Expiry date recognition using deep neural networks

2020 ◽  
Vol 13 (1) ◽  
pp. 1-17
Author(s):  
Traian Rebedea ◽  
Vlad Florea
Keyword(s):  
Neural Networks ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Speech Synthesis ◽  
Efficient Solution ◽  
Deep Neural Networks ◽  
Accuracy Improvement ◽  
Expiry Date ◽  
Food Items ◽  
Optical Character

This paper proposes a deep learning solution for optical character recognition, specifically tuned to detect expiration dates that are printed on the packaging of food items. This method can be used to reduce food waste, having a significant impact on the design of smart refrigerators and can prove especially useful for persons with vision difficulties, by combining it with a speech synthesis engine. The main problem in designing an efficient solution for expiry date recognition is the lack of a large enough dataset to train deep neural networks. To tackle this issue, we propose to use an additional dataset composed of synthetically generated images. Both the synthetic and real image datasets are detailed in the paper and we show that the proposed method offers a 9.4% accuracy improvement over using real images alone.

scholarly journals Multimodal News Article Analysis

2017 ◽  
Author(s):  
Arnau Ramisa
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Language Processing ◽  
Deep Neural Networks ◽  
High Capacity ◽  
Online News ◽  
News Article ◽  
General Knowledge ◽  
Article Analysis ◽  
The Rich

The intersection of Computer Vision and Natural Language Processing has been a hot topic of research in recent years, with results that were unthinkable only a few years ago. In view of this progress, we want to highlight online news articles as a potential next step for this area of research. The rich interrelations of text, tags, images or videos, as well as a vast corpus of general knowledge are an exciting benchmark for high-capacity models such as the deep neural networks. In this paper we present a series of tasks and baseline approaches to leverage corpus such as the BreakingNews dataset.

scholarly journals Applying deep neural networks to predict incidence and phenology of plant pests and diseases

Ecosphere ◽  
2021 ◽  
Vol 12 (10) ◽  
Author(s):  
Marc Grünig ◽  
Elisabeth Razavi ◽  
Pierluigi Calanca ◽  
Dominique Mazzi ◽  
Jan Dirk Wegner ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Pests And Diseases ◽  
Plant Pests

scholarly journals Injection of Traditional Hand-Crafted Features into Modern CNN-based Models for SAR Ship Classification: What, Why, Where, and How

2021 ◽  
Vol 13 (11) ◽  
pp. 2091
Author(s):  
Tianwen Zhang ◽  
Xiaoling Zhang
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Classification Accuracy ◽  
Classification Performance ◽  
Synthetic Aperture ◽  
Injection Technique ◽  
Accuracy Improvement ◽  
Deep Networks ◽  
Ship Classification

With the rise of artificial intelligence, many advanced synthetic aperture radar (SAR) ship classifiers based on convolutional neural networks (CNNs) have achieved better accuracies than traditional hand-crafted feature ones. However, most existing CNN-based models uncritically abandon traditional hand-crafted features, and rely excessively on abstract ones of deep networks. This may be controversial, potentially creating challenges to improve classification performance further. Therefore, in view of this situation, this paper explores preliminarily the possibility of injection of traditional hand-crafted features into modern CNN-based models to further improve SAR ship classification accuracy. Specifically, we will — (1) illustrate what this injection technique is, (2) explain why it is needed, (3) discuss where it should be applied, and (4) describe how it is implemented. Experimental results on the two open three-category OpenSARShip-1.0 and seven-category FUSAR-Ship datasets indicate that it is effective to perform injection of traditional hand-crafted features into CNN-based models to improve classification accuracy. Notably, the maximum accuracy improvement reaches 6.75%. Hence, we hold the view that it is not advisable to abandon uncritically traditional hand-crafted features, because they can also play an important role in CNN-based models.

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