scholarly journals Deep Transfer Learning for Vulnerable Road Users Detection using Smartphone Sensors Data

2020 ◽  
Vol 12 (21) ◽  
pp. 3508
Author(s):  
Mohammed Elhenawy ◽  
Huthaifa I. Ashqar ◽  
Mahmoud Masoud ◽  
Mohammed H. Almannaa ◽  
Andry Rakotonirainy ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Classification Accuracy ◽  
High Accuracy ◽  
Training Time ◽  
Road Users ◽  
Smartphone Sensors ◽  
Vulnerable Road Users ◽  
And Behavior

As the Autonomous Vehicle (AV) industry is rapidly advancing, the classification of non-motorized (vulnerable) road users (VRUs) becomes essential to ensure their safety and to smooth operation of road applications. The typical practice of non-motorized road users’ classification usually takes significant training time and ignores the temporal evolution and behavior of the signal. In this research effort, we attempt to detect VRUs with high accuracy be proposing a novel framework that includes using Deep Transfer Learning, which saves training time and cost, to classify images constructed from Recurrence Quantification Analysis (RQA) that reflect the temporal dynamics and behavior of the signal. Recurrence Plots (RPs) were constructed from low-power smartphone sensors without using GPS data. The resulted RPs were used as inputs for different pre-trained Convolutional Neural Network (CNN) classifiers including constructing 227 × 227 images to be used for AlexNet and SqueezeNet; and constructing 224 × 224 images to be used for VGG16 and VGG19. Results show that the classification accuracy of Convolutional Neural Network Transfer Learning (CNN-TL) reaches 98.70%, 98.62%, 98.71%, and 98.71% for AlexNet, SqueezeNet, VGG16, and VGG19, respectively. Moreover, we trained resnet101 and shufflenet for a very short time using one epoch of data and then used them as weak learners, which yielded 98.49% classification accuracy. The results of the proposed framework outperform other results in the literature (to the best of our knowledge) and show that using CNN-TL is promising for VRUs classification. Because of its relative straightforwardness, ability to be generalized and transferred, and potential high accuracy, we anticipate that this framework might be able to solve various problems related to signal classification.

scholarly journals Vulnerable Road Users Classification of Smartphone Sensors Data Using Deep Transfer Learning

2020 ◽  
Author(s):  
Mohammed Elhenawy ◽  
Huthaifa Ashqar ◽  
Mahmoud Masoud ◽  
Mohammed Almannaa ◽  
Andry Rakotonirainy ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
High Accuracy ◽  
Training Time ◽  
Road Users ◽  
Smartphone Sensors ◽  
Vulnerable Road Users ◽  
And Behavior

As the Autonomous Vehicle (AV) industry is rapidly advancing, classification of non-motorized (vulnerable) road users (VRUs) becomes essential to ensure their safety and to smooth operation of road applications. The typical practice of non-motorized road users’ classification usually takes numerous training time and ignores the temporal evolution and behavior of the signal. In this research effort, we attempt to detect VRUs with high accuracy be proposing a novel framework that includes using Deep Transfer Learning, which saves training time and cost, to classify images constructed from Recurrence Quantification Analysis (RQA) that reflect the temporal dynamics and behavior of the signal. Recurrence Plots (RPs) were constructed from low-power smartphone sensors without using GPS data. The resulted RPs were used as inputs for different pre-trained Convolutional Neural Network (CNN) classifiers including constructing 227×227 images to be used for AlexNet and SqueezeNet; and constructing 224×224 images to be used for VGG16 and VGG19. Results show that the classification accuracy of Convolutional Neural Network Transfer Learning (CNN-TL) reaches 98.70%, 98.62%, 98.71%, and 98.71% for AlexNet, SqueezeNet, VGG16, and VGG19, respectively. The results of the proposed framework outperform other results in the literature (to the best of our knowledge) and show that using CNN-TL is promising for VRUs classification. Because of its relative straightforwardness, ability to be generalized and transferred, and potential high accuracy, we anticipate that this framework might be able to solve various problems related to signal classification.

scholarly journals Surface EMG-Based Instantaneous Hand Gesture Recognition Using Convolutional Neural Network with the Transfer Learning Method

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2540
Author(s):  
Zhipeng Yu ◽  
Jianghai Zhao ◽  
Yucheng Wang ◽  
Linglong He ◽  
Shaonan Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Gesture Recognition ◽  
Recognition System ◽  
Surface Emg ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Training Time ◽  
Generalization Ability

In recent years, surface electromyography (sEMG)-based human–computer interaction has been developed to improve the quality of life for people. Gesture recognition based on the instantaneous values of sEMG has the advantages of accurate prediction and low latency. However, the low generalization ability of the hand gesture recognition method limits its application to new subjects and new hand gestures, and brings a heavy training burden. For this reason, based on a convolutional neural network, a transfer learning (TL) strategy for instantaneous gesture recognition is proposed to improve the generalization performance of the target network. CapgMyo and NinaPro DB1 are used to evaluate the validity of our proposed strategy. Compared with the non-transfer learning (non-TL) strategy, our proposed strategy improves the average accuracy of new subject and new gesture recognition by 18.7% and 8.74%, respectively, when up to three repeated gestures are employed. The TL strategy reduces the training time by a factor of three. Experiments verify the transferability of spatial features and the validity of the proposed strategy in improving the recognition accuracy of new subjects and new gestures, and reducing the training burden. The proposed TL strategy provides an effective way of improving the generalization ability of the gesture recognition system.

scholarly journals A Single Target Grasp Detection Network Based on Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Longzhi Zhang ◽  
Dongmei Wu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
High Accuracy ◽  
Experimental Results ◽  
Detection Accuracy ◽  
Single Object ◽  
Single Target ◽  
High Detection ◽  
Good Detection

Grasp detection based on convolutional neural network has gained some achievements. However, overfitting of multilayer convolutional neural network still exists and leads to poor detection precision. To acquire high detection accuracy, a single target grasp detection network that generalizes the fitting of angle and position, based on the convolution neural network, is put forward here. The proposed network regards the image as input and grasping parameters including angle and position as output, with the detection manner of end-to-end. Particularly, preprocessing dataset is to achieve the full coverage to input of model and transfer learning is to avoid overfitting of network. Importantly, a series of experimental results indicate that, for single object grasping, our network has good detection results and high accuracy, which proves that the proposed network has strong generalization in direction and category.

scholarly journals Voiceprint Identification for Limited Dataset Using the Deep Migration Hybrid Model Based on Transfer Learning

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2399 ◽  
Author(s):  
Cunwei Sun ◽  
Yuxin Yang ◽  
Chang Wen ◽  
Kai Xie ◽  
Fangqing Wen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Hybrid Model ◽  
Data Augmentation ◽  
Small Sample ◽  
Restricted Boltzmann Machine ◽  
Small Samples ◽  
Boltzmann Machine ◽  
Training Time

The convolutional neural network (CNN) has made great strides in the area of voiceprint recognition; but it needs a huge number of data samples to train a deep neural network. In practice, it is too difficult to get a large number of training samples, and it cannot achieve a better convergence state due to the limited dataset. In order to solve this question, a new method using a deep migration hybrid model is put forward, which makes it easier to realize voiceprint recognition for small samples. Firstly, it uses Transfer Learning to transfer the trained network from the big sample voiceprint dataset to our limited voiceprint dataset for the further training. Fully-connected layers of a pre-training model are replaced by restricted Boltzmann machine layers. Secondly, the approach of Data Augmentation is adopted to increase the number of voiceprint datasets. Finally, we introduce fast batch normalization algorithms to improve the speed of the network convergence and shorten the training time. Our new voiceprint recognition approach uses the TLCNN-RBM (convolutional neural network mixed restricted Boltzmann machine based on transfer learning) model, which is the deep migration hybrid model that is used to achieve an average accuracy of over 97%, which is higher than that when using either CNN or the TL-CNN network (convolutional neural network based on transfer learning). Thus, an effective method for a small sample of voiceprint recognition has been provided.

A Modified Convolutional Neural Network for Resting-State EEG-Based Schizophrenia Classification with Weighted Electrodes

2020 ◽  
Vol 10 (3) ◽  
pp. 681-687
Author(s):  
Danyang Ma ◽  
Genke Yang ◽  
Zeya Li ◽  
Haichun Liu ◽  
Changchun Pan ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Resting State ◽  
Classification Accuracy ◽  
Pathological Study ◽  
Severe Mental Disorder ◽  
Time Consumption ◽  
Weight Value ◽  
And Behavior

Schizophrenia is a severe mental disorder that can result in hallucinations, delusions, and extremely disordered thinking and behavior. While electroencephalography (EEG) has been used as an auxiliary tool for diagnostic purposes in several recent studies, all EEG channels are treated homogeneously without addressing the dominance of certain channels. The main purpose of this study is to obtain the weight value of each channel as the quantitative representation of influence of each scalp area on the classification of schizophrenia phases, and then to apply the weight values to improve the accuracy of classification. We propose a new convolutional neural network (CNN) structure based on AlexNet to derive weight values as weight layer and classify the samples better. Our results show that the modified CNN structure achieves better performance in terms of time consumption and classification accuracy compared with the original classifier. Also, the visualization of the weight layer in our model indicates possible correlations between scalp areas and schizophrenia conditions, which may benefit future pathological study.

scholarly journals Improving Deep Learning-Based UWB LOS/NLOS Identification with Transfer Learning: An Empirical Approach

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1714
Author(s):  
JiWoong Park ◽  
SungChan Nam ◽  
HongBeom Choi ◽  
YoungEun Ko ◽  
Young-Bae Ko
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Transfer Learning ◽  
Classification Accuracy ◽  
Ultra Wideband ◽  
Line Of Sight ◽  
Training Time ◽  
Identification Scheme ◽  
Office Environments ◽  
Non Line Of Sight

This paper presents an improved ultra-wideband (UWB) line of sight (LOS)/non-line of sight (NLOS) identification scheme based on a hybrid method of deep learning and transfer learning. Previous studies have limitations, in that the classification accuracy significantly decreases in an unknown place. To solve this problem, we propose a transfer learning-based NLOS identification method for classifying the NLOS conditions of the UWB signal in an unmeasured environment. Both the multilayer perceptron and convolutional neural network (CNN) are introduced as classifiers for NLOS conditions. We evaluate the proposed scheme by conducting experiments in both measured and unmeasured environments. Channel data were measured using a Decawave EVK1000 in two similar indoor office environments. In the unmeasured environment, the existing CNN method showed an accuracy of approximately 44%, but when the proposed scheme was applied to the CNN, it showed an accuracy of up to 98%. The training time of the proposed scheme was measured to be approximately 48 times faster than that of the existing CNN. When comparing the proposed scheme with learning a new CNN in an unmeasured environment, the proposed scheme demonstrated an approximately 10% higher accuracy and approximately five times faster training time.

scholarly journals Hyperspectral Image Classification Based on a Shuffled Group Convolutional Neural Network with Transfer Learning

2020 ◽  
Vol 12 (11) ◽  
pp. 1780 ◽  
Author(s):  
Yao Liu ◽  
Lianru Gao ◽  
Chenchao Xiao ◽  
Ying Qu ◽  
Ke Zheng ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Classification Accuracy ◽  
Hyperspectral Image ◽  
Classification Performance ◽  
Training Dataset ◽  
Hyperspectral Image Classification ◽  
Different Levels

Convolutional neural networks (CNNs) have been widely applied in hyperspectral imagery (HSI) classification. However, their classification performance might be limited by the scarcity of labeled data to be used for training and validation. In this paper, we propose a novel lightweight shuffled group convolutional neural network (abbreviated as SG-CNN) to achieve efficient training with a limited training dataset in HSI classification. SG-CNN consists of SG conv units that employ conventional and atrous convolution in different groups, followed by channel shuffle operation and shortcut connection. In this way, SG-CNNs have less trainable parameters, whilst they can still be accurately and efficiently trained with fewer labeled samples. Transfer learning between different HSI datasets is also applied on the SG-CNN to further improve the classification accuracy. To evaluate the effectiveness of SG-CNNs for HSI classification, experiments have been conducted on three public HSI datasets pretrained on HSIs from different sensors. SG-CNNs with different levels of complexity were tested, and their classification results were compared with fine-tuned ShuffleNet2, ResNeXt, and their original counterparts. The experimental results demonstrate that SG-CNNs can achieve competitive classification performance when the amount of labeled data for training is poor, as well as efficiently providing satisfying classification results.

Small aircraft detection using deep learning

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Emre Kiyak ◽  
Gulay Unal
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Fine Tuning ◽  
Deep Convolutional Neural Networks ◽  
Content Type ◽  
Training Time

Purpose The paper aims to address the tracking algorithm based on deep learning and four deep learning tracking models developed. They compared with each other to prevent collision and to obtain target tracking in autonomous aircraft. Design/methodology/approach First, to follow the visual target, the detection methods were used and then the tracking methods were examined. Here, four models (deep convolutional neural networks (DCNN), deep convolutional neural networks with fine-tuning (DCNNFN), transfer learning with deep convolutional neural network (TLDCNN) and fine-tuning deep convolutional neural network with transfer learning (FNDCNNTL)) were developed. Findings The training time of DCNN took 9 min 33 s, while the accuracy percentage was calculated as 84%. In DCNNFN, the training time of the network was calculated as 4 min 26 s and the accuracy percentage was 91%. The training of TLDCNN) took 34 min and 49 s and the accuracy percentage was calculated as 95%. With FNDCNNTL, the training time of the network was calculated as 34 min 33 s and the accuracy percentage was nearly 100%. Originality/value Compared to the results in the literature ranging from 89.4% to 95.6%, using FNDCNNTL, better results were found in the paper.

scholarly journals Convolutional Neural Network Coupled with a Transfer-Learning Approach for Time-Series Flood Predictions

Water ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 96 ◽  
Author(s):  
Nobuaki Kimura ◽  
Ikuo Yoshinaga ◽  
Kenji Sekijima ◽  
Issaku Azechi ◽  
Daichi Baba
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Learning Approach ◽  
Target Domain ◽  
Training Time ◽  
Source Domain ◽  
Flood Model

East Asian regions in the North Pacific have recently experienced severe riverine flood disasters. State-of-the-art neural networks are currently utilized as a quick-response flood model. Neural networks typically require ample time in the training process because of the use of numerous datasets. To reduce the computational costs, we introduced a transfer-learning approach to a neural-network-based flood model. For a concept of transfer leaning, once the model is pretrained in a source domain with large datasets, it can be reused in other target domains. After retraining parts of the model with the target domain datasets, the training time can be reduced due to reuse. A convolutional neural network (CNN) was employed because the CNN with transfer learning has numerous successful applications in two-dimensional image classification. However, our flood model predicts time-series variables (e.g., water level). The CNN with transfer learning requires a conversion tool from time-series datasets to image datasets in preprocessing. First, the CNN time-series classification was verified in the source domain with less than 10% errors for the variation in water level. Second, the CNN with transfer learning in the target domain efficiently reduced the training time by 1/5 of and a mean error difference by 15% of those obtained by the CNN without transfer learning, respectively. Our method can provide another novel flood model in addition to physical-based models.

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