scholarly journals SIMULATION-BASED DATA AUGMENTATION USING PHYSICAL PRIORS FOR NOISE FILTERING DEEP NEURAL NETWORK

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 247-254
Author(s):  
M. Jameela ◽  
L. Chen ◽  
A. Sit ◽  
J. Yoo ◽  
C. Verheggen ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Data Augmentation ◽  
Pulse Repetition Frequency ◽  
Single Pulse ◽  
Airborne Lidar ◽  
Noise Filtering ◽  
Learning Networks ◽  
Simulation Based ◽  
Multiple Pulses

Abstract. LiDAR (Light Detection and Ranging) mounted with static and mobile vehicles has been rapidly adopted as a primary sensor for mapping natural and built environments for a range of civil and military applications. Recently, technology advancement in electro-optical engineering enables acquiring laser returns at high pulse repetition frequency (PRF) from 100Hz to 2MHz for airborne LiDAR, which leads to an increase in the density of 3D point cloud significantly. Traditional systems with lower PRF had a single pulse-in-air zone (PIA) big enough to avoid a mismatch between pulse pair at the receiver. Modern multiple pulses-in-air (MPIA) technology ensures multiple windows of operational ranges for single flight line and no blind-zones; downside of the technology is projection of atmospheric returns closer to same PIA zone of neighbouring ground points and more likely to be overlapping with objects of interest. These characteristics of noise compromise the quality of the scene and encourage usage of noise filtering neural network as existing filters are not effective. A noise filtering deep neural network requires a considerable volume of the diverse annotated dataset, which is expensive. We developed simulation for data augmentation based on physical priors and Gaussian generative function. Our study compares deep learning networks for noise filtering and shows performance gain on 3D U-Net. Then, we evaluate 3D U-Net for simulation-based data augmentation, which shows an increase in precision and F1-score. We also provide an analysis of the underline spatial distribution of points and their impact on data augmentation, and noise filtering.

scholarly journals An efficient technique for CT scan images classification of COVID-19

2020 ◽  
pp. 1-14
Author(s):  
Esraa Hassan ◽  
Noha A. Hikal ◽  
Samir Elmuogy
Keyword(s):  
Neural Network ◽  
Diagnostic Tool ◽  
Deep Neural Network ◽  
Data Augmentation ◽  
Performance Metrics ◽  
Classification Model ◽  
Data Set ◽  
Training Models ◽  
The Earth ◽  
Diagnostic Time

Nowadays, Coronavirus (COVID-19) considered one of the most critical pandemics in the earth. This is due its ability to spread rapidly between humans as well as animals. COVID_19 expected to outbreak around the world, around 70 % of the earth population might infected with COVID-19 in the incoming years. Therefore, an accurate and efficient diagnostic tool is highly required, which the main objective of our study. Manual classification was mainly used to detect different diseases, but it took too much time in addition to the probability of human errors. Automatic image classification reduces doctors diagnostic time, which could save human’s life. We propose an automatic classification architecture based on deep neural network called Worried Deep Neural Network (WDNN) model with transfer learning. Comparative analysis reveals that the proposed WDNN model outperforms by using three pre-training models: InceptionV3, ResNet50, and VGG19 in terms of various performance metrics. Due to the shortage of COVID-19 data set, data augmentation was used to increase the number of images in the positive class, then normalization used to make all images have the same size. Experimentation is done on COVID-19 dataset collected from different cases with total 2623 where (1573 training,524 validation,524 test). Our proposed model achieved 99,046, 98,684, 99,119, 98,90 In terms of Accuracy, precision, Recall, F-score, respectively. The results are compared with both the traditional machine learning methods and those using Convolutional Neural Networks (CNNs). The results demonstrate the ability of our classification model to use as an alternative of the current diagnostic tool.

scholarly journals Robust Approach to Supervised Deep Neural Network Training for Real-Time Object Classification in Cluttered Indoor Environment

2021 ◽  
Vol 11 (15) ◽  
pp. 7148
Author(s):  
Bedada Endale ◽  
Abera Tullu ◽  
Hayoung Shi ◽  
Beom-Soo Kang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real Time ◽  
Network Architecture ◽  
Input Data ◽  
Deep Neural Network ◽  
Data Augmentation ◽  
Object Classification ◽  
Training Data ◽  
Gradient Descent Algorithm

Unmanned aerial vehicles (UAVs) are being widely utilized for various missions: in both civilian and military sectors. Many of these missions demand UAVs to acquire artificial intelligence about the environments they are navigating in. This perception can be realized by training a computing machine to classify objects in the environment. One of the well known machine training approaches is supervised deep learning, which enables a machine to classify objects. However, supervised deep learning comes with huge sacrifice in terms of time and computational resources. Collecting big input data, pre-training processes, such as labeling training data, and the need for a high performance computer for training are some of the challenges that supervised deep learning poses. To address these setbacks, this study proposes mission specific input data augmentation techniques and the design of light-weight deep neural network architecture that is capable of real-time object classification. Semi-direct visual odometry (SVO) data of augmented images are used to train the network for object classification. Ten classes of 10,000 different images in each class were used as input data where 80% were for training the network and the remaining 20% were used for network validation. For the optimization of the designed deep neural network, a sequential gradient descent algorithm was implemented. This algorithm has the advantage of handling redundancy in the data more efficiently than other algorithms.

scholarly journals MFDNN: Multi-channel feature deep neural network algorithm to identify Covid19 chest X-ray images

2021 ◽  
Author(s):  
Liangrui Pan ◽  
boya ji ◽  
Xiaoqi wang ◽  
shaoliang peng
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Test Accuracy ◽  
Learning Networks ◽  
X Ray ◽  
Network Algorithm ◽  
Life Saving ◽  
Chest X Ray ◽  
Neural Network Algorithm

The use of chest X-ray images (CXI) to detect Severe Acute Respiratory Syndrome Coronavirus 2 (SARS CoV-2) caused by Coronavirus Disease 2019 (COVID-19) is life-saving important for both patients and doctors. This research proposed a multi-channel feature deep neural network algorithm to screen people infected with COVID-19. The algorithm integrates data oversampling technology and a multi-channel feature deep neural network model to carry out the training process in an end-to-end manner. In the experiment, we used a publicly available CXI database with 10,192 Normal, 6012 Lung Opacity (Non-COVID lung infection), and 1345 Viral Pneumonia images. Compared with traditional deep learning models (Densenet201, ResNet50, VGG19, GoogLeNet), the MFDNN model obtains an average test accuracy of 93.19% in all data. Furthermore, in each type of screening, the precision, recall, and F1 Score of the MFDNN model are also better than traditional deep learning networks. Secondly, compared with the latest CoroDet model, the MFDNN algorithm is 1.91% higher than the CoroDet model in the experiment of detecting the four categories of COVID19 infected persons. Finally, our experimental code will be placed at https://github.com/panliangrui/covid19.

A Similarity-Based Object Classification Using Deep Neural Networks

2019 ◽  
pp. 197-219
Author(s):  
Parvathi R. ◽  
Pattabiraman V.
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Search Algorithm ◽  
Random Projection ◽  
Learning Networks ◽  
Learning Models ◽  
External Conditions ◽  
And Training

This chapter proposes a hybrid method for classification of the objects based on deep neural network and a similarity-based search algorithm. The objects are pre-processed with external conditions. After pre-processing and training different deep learning networks with the object dataset, the authors compare the results to find the best model to improve the accuracy of the results based on the features of object images extracted from the feature vector layer of a neural network. RPFOREST (random projection forest) model is used to predict the approximate nearest images. ResNet50, InceptionV3, InceptionV4, and DenseNet169 models are trained with this dataset. A proposal for adaptive finetuning of the deep learning models by determining the number of layers required for finetuning with the help of the RPForest model is given, and this experiment is conducted using the Xception model.

Image Scene Analysis Based on Improved FCN Model

2021 ◽  
pp. 2152020
Author(s):  
Weijie Yang ◽  
Yueting Hui
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Data Augmentation ◽  
Semantic Segmentation ◽  
Scene Analysis ◽  
Model Structure ◽  
Multi Scale ◽  
Class Labels ◽  
Small Targets ◽  
Scene Content

Image scene analysis is to analyze image scene content through image semantic segmentation, which can identify the categories and positions of different objects in an image. However, due to the loss of spatial detail information, the accuracy of image scene analysis is often affected, resulting in rough edges of FCN, inconsistent class labels of target regions and missing small targets. To address these problems, this paper increases the receptive field, conducts multi-scale fusion and changes the weight of different sensitive channels, so as to improve the feature discrimination and maintain or restore spatial detail information. Furthermore, the deep neural network FCN is used to build the base model of semantic segmentation. The ASPP, data augmentation, SENet, decoder and global pooling are added to the baseline to optimize the model structure and improve the effect of semantic segmentation. Finally, the more accurate results of scene analysis are obtained.

scholarly journals Human Skeleton Data Augmentation for Person Identification over Deep Neural Network

2020 ◽  
Vol 10 (14) ◽  
pp. 4849 ◽  
Author(s):  
Beom Kwon ◽  
Sanghoon Lee
Keyword(s):  
Neural Network ◽  
Human Body ◽  
Deep Neural Network ◽  
Data Augmentation ◽  
Bilateral Symmetry ◽  
Person Identification ◽  
Human Skeleton ◽  
Average Accuracy ◽  
Anthropometric Features ◽  
Public Datasets

With the advancement in pose estimation techniques, skeleton-based person identification has recently received considerable attention in many applications. In this study, a skeleton-based person identification method using a deep neural network (DNN) is investigated. In this method, anthropometric features extracted from the human skeleton sequence are used as the input to the DNN. However, training the DNN with insufficient training datasets makes the network unstable and may lead to overfitting during the training phase, causing significant performance degradation in the testing phase. To cope with a shortage in the dataset, we investigate novel data augmentation for skeleton-based person identification by utilizing the bilateral symmetry of the human body. To achieve this, augmented vectors are generated by sharing the anthropometric features extracted from one side of the human body with the other and vice versa. Thereby, the total number of anthropometric feature vectors is increased by 256 times, which enables the DNN to be trained while avoiding overfitting. The simulation results demonstrate that the average accuracy of person identification is remarkably improved up to 100% based on the augmentation on public datasets.

scholarly journals Dynamic Wide and Deep Neural Network for Hyperspectral Image Classification

2021 ◽  
Vol 13 (13) ◽  
pp. 2575
Author(s):  
Jiangbo Xi ◽  
Ming Cong ◽  
Okan K. Ersoy ◽  
Weibao Zou ◽  
Chaoying Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Hyperspectral Image ◽  
Least Squares Method ◽  
Support Vector ◽  
Learning Networks ◽  
Learning Methods ◽  
Sliding Windows ◽  
Deep Layers

Recently, deep learning has been successfully and widely used in hyperspectral image (HSI) classification. Considering the difficulty of acquiring HSIs, there are usually a small number of pixels used as the training instances. Therefore, it is hard to fully use the advantages of deep learning networks; for example, the very deep layers with a large number of parameters lead to overfitting. This paper proposed a dynamic wide and deep neural network (DWDNN) for HSI classification, which includes multiple efficient wide sliding window and subsampling (EWSWS) networks and can grow dynamically according to the complexity of the problems. The EWSWS network in the DWDNN was designed both in the wide and deep direction with transform kernels as hidden units. These multiple layers of kernels can extract features from the low to high level, and because they are extended in the wide direction, they can learn features more steadily and smoothly. The sliding windows with the stride and subsampling were designed to reduce the dimension of the features for each layer; therefore, the computational load was reduced. Finally, all the weights were only from the fully connected layer, and the iterative least squares method was used to compute them easily. The proposed DWDNN was tested with several HSI data including the Botswana, Pavia University, and Salinas remote sensing datasets with different numbers of instances (from small to big). The experimental results showed that the proposed method had the highest test accuracies compared to both the typical machine learning methods such as support vector machine (SVM), multilayer perceptron (MLP), radial basis function (RBF), and the recently proposed deep learning methods including the 2D convolutional neural network (CNN) and the 3D CNN designed for HSI classification.

scholarly journals Real-Time Detection for Wheat Head Applying Deep Neural Network

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 191
Author(s):  
Bo Gong ◽  
Daji Ergu ◽  
Ying Cai ◽  
Bo Ma
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Deep Neural Network ◽  
Data Augmentation ◽  
Detection Methods ◽  
Convolutional Network ◽  
Head Detection ◽  
Real Time Detection ◽  
Low Efficiency ◽  
Basic Network

Wheat head detection can estimate various wheat traits, such as density, health, and the presence of wheat head. However, traditional detection methods have a huge array of problems, including low efficiency, strong subjectivity, and poor accuracy. In this paper, a method of wheat-head detection based on a deep neural network is proposed to enhance the speed and accuracy of detection. The YOLOv4 is taken as the basic network. The backbone part in the basic network is enhanced by adding dual spatial pyramid pooling (SPP) networks to improve the ability of feature learning and increase the receptive field of the convolutional network. Multilevel features are obtained by a multipath neck part using a top-down to bottom-up strategy. Finally, YOLOv3′s head structures are used to predict the boxes of wheat heads. For training images, some data augmentation technologies are used. The experimental results demonstrate that the proposed method has a significant advantage in accuracy and speed. The mean average precision of our method is 94.5%, and the detection speed is 71 FPS that can achieve the effect of real-time detection.

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