scholarly journals Weed Detection Model Using the Generative Adversarial Network and Deep Convolutional Neural Network

2021 ◽  
Author(s):  
S. Anthoniraj ◽  
P. Karthikeyan ◽  
V. Vivek
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Crop Production ◽  
Deep Convolutional Neural Network ◽  
Generative Adversarial Networks ◽  
Weed Detection ◽  
Generative Adversarial Network ◽  
Detection Model ◽  
Adversarial Network ◽  
Improved Performance

Agriculture crop demand is increasing day by day because of population. Crop production can be increased by removing weeds in the agriculture field. However, weed detection is a complicated problem in the agriculture field. The main objective of this paper is to improve the accuracy of weed detection by combining generative adversarial networks and convolutional neural networks. We have implemented deep learning models, namely Generative Adversarial Network and Deep Convolutional Neural Network (GAN-DCNN), AlexNet, VGG16, ResNet50, and Google Net perform the detection of the weed. A generative Adversarial Network generates the weed image, and Deep Convolutional Neural Network detects the weed in the image. GAN-DCNN method outperforms than existing weed detection method. Simulation results confirm that the proposed GAN-DCNN has improved performance with a maximum weed detection rate of 87.12 and 96.34 accuracies.

scholarly journals Convolutional Neural Network Audio Classifier for Alarm Sound Detection

2019 ◽  
Vol 8 (6) ◽  
pp. 4554-4557
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Sound Recognition ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Differential Network ◽  
Sound Detection ◽  
Long Short Term Memory ◽  
Lstm Network

Neural Networks (ANN) has evolved through many stages in the last three decades with many researchers contributing in this challenging field. With the power of math complex problems can also be solved by ANNs. ANNs like Convolutional Neural Network (CNN), Deep Neural network, Generative Adversarial Network (GAN), Long Short Term Memory (LSTM) network, Recurrent Neural Network (RNN), Ordinary Differential Network etc., are playing promising roles in many MNCs and IT industries for their predictions and accuracy. In this paper, Convolutional Neural Network is used for prediction of Beep sounds in high noise levels. Based on Supervised Learning, the research is developed the best CNN architecture for Beep sound recognition in noisy situations. The proposed method gives better results with an accuracy of 96%. The prototype is tested with few architectures for the training and test data out of which a two layer CNN classifier predictions were the best.

scholarly journals Cloud Removal with Fusion of High Resolution Optical and SAR Images Using Generative Adversarial Networks

2020 ◽  
Vol 12 (1) ◽  
pp. 191 ◽  
Author(s):  
Jianhao Gao ◽  
Qiangqiang Yuan ◽  
Jie Li ◽  
Hai Zhang ◽  
Xin Su
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Optical Image ◽  
Optical Data ◽  
Generative Adversarial Networks ◽  
Second Step ◽  
Missing Information ◽  
Sar Images ◽  
Generative Adversarial Network ◽  
Cloud Removal

The existence of clouds is one of the main factors that contributes to missing information in optical remote sensing images, restricting their further applications for Earth observation, so how to reconstruct the missing information caused by clouds is of great concern. Inspired by the image-to-image translation work based on convolutional neural network model and the heterogeneous information fusion thought, we propose a novel cloud removal method in this paper. The approach can be roughly divided into two steps: in the first step, a specially designed convolutional neural network (CNN) translates the synthetic aperture radar (SAR) images into simulated optical images in an object-to-object manner; in the second step, the simulated optical image, together with the SAR image and the optical image corrupted by clouds, is fused to reconstruct the corrupted area by a generative adversarial network (GAN) with a particular loss function. Between the first step and the second step, the contrast and luminance of the simulated optical image are randomly altered to make the model more robust. Two simulation experiments and one real-data experiment are conducted to confirm the effectiveness of the proposed method on Sentinel 1/2, GF 2/3 and airborne SAR/optical data. The results demonstrate that the proposed method outperforms state-of-the-art algorithms that also employ SAR images as auxiliary data.

scholarly journals Automatic Target Recognition for Synthetic Aperture Radar Images Based on Super-Resolution Generative Adversarial Network and Deep Convolutional Neural Network

2019 ◽  
Vol 11 (2) ◽  
pp. 135 ◽  
Author(s):  
Xiaoran Shi ◽  
Feng Zhou ◽  
Shuang Yang ◽  
Zijing Zhang ◽  
Tao Su
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Target Recognition ◽  
Super Resolution ◽  
Automatic Target Recognition ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Feature Characterization

Aiming at the problem of the difficulty of high-resolution synthetic aperture radar (SAR) image acquisition and poor feature characterization ability of low-resolution SAR image, this paper proposes a method of an automatic target recognition method for SAR images based on a super-resolution generative adversarial network (SRGAN) and deep convolutional neural network (DCNN). First, the threshold segmentation is utilized to eliminate the SAR image background clutter and speckle noise and accurately extract target area of interest. Second, the low-resolution SAR image is enhanced through SRGAN to improve the visual resolution and the feature characterization ability of target in the SAR image. Third, the automatic classification and recognition for SAR image is realized by using DCNN with good generalization performance. Finally, the open data set, moving and stationary target acquisition and recognition, is utilized and good recognition results are obtained under standard operating condition and extended operating conditions, which verify the effectiveness, robustness, and good generalization performance of the proposed method.

scholarly journals Design of Painting Art Style Rendering System Based on Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xingyu Xie ◽  
Bin Lv
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Generative Adversarial Network ◽  
Convolutional Network ◽  
Data Set ◽  
Adversarial Network ◽  
Paired Samples ◽  
Network Generator ◽  
The Stability ◽  
Art Style

Convolutional Neural Network- (CNN-) based GAN models mainly suffer from problems such as data set limitation and rendering efficiency in the segmentation and rendering of painting art. In order to solve these problems, this paper uses the improved cycle generative adversarial network (CycleGAN) to render the current image style. This method replaces the deep residual network (ResNet) of the original network generator with a dense connected convolutional network (DenseNet) and uses the perceptual loss function for adversarial training. The painting art style rendering system built in this paper is based on perceptual adversarial network (PAN) for the improved CycleGAN that suppresses the limitation of the network model on paired samples. The proposed method also improves the quality of the image generated by the artistic style of painting and further improves the stability and speeds up the network convergence speed. Experiments were conducted on the painting art style rendering system based on the proposed model. Experimental results have shown that the image style rendering method based on the perceptual adversarial error to improve the CycleGAN + PAN model can achieve better results. The PSNR value of the generated image is increased by 6.27% on average, and the SSIM values are all increased by about 10%. Therefore, the improved CycleGAN + PAN image painting art style rendering method produces better painting art style images, which has strong application value.

scholarly journals Copy-Move Forgery Detection and Localization Using a Generative Adversarial Network and Convolutional Neural-Network

Information ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 286
Author(s):  
Younis Abdalla ◽  
M. Tariq Iqbal ◽  
Mohamed Shehata
Keyword(s):  
Neural Network ◽  
New Technologies ◽  
Generative Adversarial Networks ◽  
Detection Accuracy ◽  
Forgery Detection ◽  
Generative Adversarial Network ◽  
Neural Network Approach ◽  
Adversarial Network ◽  
Copy Move Forgery Detection ◽  
Detection And Localization

The problem of forged images has become a global phenomenon that is spreading mainly through social media. New technologies have provided both the means and the support for this phenomenon, but they are also enabling a targeted response to overcome it. Deep convolution learning algorithms are one such solution. These have been shown to be highly effective in dealing with image forgery derived from generative adversarial networks (GANs). In this type of algorithm, the image is altered such that it appears identical to the original image and is nearly undetectable to the unaided human eye as a forgery. The present paper investigates copy-move forgery detection using a fusion processing model comprising a deep convolutional model and an adversarial model. Four datasets are used. Our results indicate a significantly high detection accuracy performance (~95%) exhibited by the deep learning CNN and discriminator forgery detectors. Consequently, an end-to-end trainable deep neural network approach to forgery detection appears to be the optimal strategy. The network is developed based on two-branch architecture and a fusion module. The two branches are used to localize and identify copy-move forgery regions through CNN and GAN.

scholarly journals Speech Emotion Recognition using Data Augmentation Method by Cycle-Generative Adversarial Networks

2021 ◽  
Author(s):  
Arash Shilandari ◽  
Hossein Marvi ◽  
Hossein Khosravi
Keyword(s):  
Neural Network ◽  
Emotion Recognition ◽  
Speech Processing ◽  
Data Augmentation ◽  
Generative Adversarial Networks ◽  
Speech Emotion Recognition ◽  
Support Vector ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks

Nowadays, and with the mechanization of life, speech processing has become so crucial for the interaction between humans and machines. Deep neural networks require a database with enough data for training. The more features are extracted from the speech signal, the more samples are needed to train these networks. Adequate training of these networks can be ensured when there is access to sufficient and varied data in each class. If there is not enough data; it is possible to use data augmentation methods to obtain a database with enough samples. One of the obstacles to developing speech emotion recognition systems is the Data sparsity problem in each class for neural network training. The current study has focused on making a cycle generative adversarial network for data augmentation in a system for speech emotion recognition. For each of the five emotions employed, an adversarial generating network is designed to generate data that is very similar to the main data in that class, as well as differentiate the emotions of the other classes. These networks are taught in an adversarial way to produce feature vectors like each class in the space of the main feature, and then they add to the training sets existing in the database to train the classifier network. Instead of using the common cross-entropy error to train generative adversarial networks and to remove the vanishing gradient problem, Wasserstein Divergence has been used to produce high-quality artificial samples. The suggested network has been tested to be applied for speech emotion recognition using EMODB as training, testing, and evaluating sets, and the quality of artificial data evaluated using two Support Vector Machine (SVM) and Deep Neural Network (DNN) classifiers. Moreover, it has been revealed that extracting and reproducing high-level features from acoustic features, speech emotion recognition with separating five primary emotions has been done with acceptable accuracy.

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