Plant Leaf Classification using Convolutional Neural Network

2020 ◽  
Vol 13 ◽  
Author(s):  
Nidhi ◽  
Jay Kant Pratap Singh Yadav
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Data ◽  
Learning Ability ◽  
Training Time ◽  
Automatic Feature Extraction ◽  
Computer Vision Applications ◽  
Shape Characteristics ◽  
And Training

Introduction: Convolutional Neural Network (CNNet) has proven the indispensable system in order to perform the recognition and classification tasks in different computer vision applications. The purpose of this study is to exploit the marvelous learning ability of CNNet in the image classification field. Method: In order to circumvent the overfitting issues and to enhance the generalization potential of the proposed FLCNNet, augmentation has been performed on the Flavia dataset that impose translation and rotation techniques to perform the augmentation with the transformed leaves having the same labels as the original ones. Both the classification models executed using; one without augmentation and one with the augmentation data are compared to check the effectiveness of the augmentation hence the aim of the proposed work. Moreover, Edge detection technique has been applied to extract the shape of the leaf images, in order to classify them accordingly. Thereafter, the FLCNNet is trained and tested for the dataset, with and without augmentation. Results: The results are gathered in terms of accuracy and training time for both datasets. The Augmented dataset (dataset 2) has been found effective and more feasible for classification without misguiding the network to learn (avoid overfitting) as compared to the dataset without augmentation (dataset 1). Conclusion: This paper proposed the Five Layer Convolution Neural Network (FLCNNet) method to classify plant leaves based on their shape. This approach can classify 8 types of leaves using automatic feature extraction, by utilizing their shape characteristics. To avoid the overfitting condition and make the performance better. We aimed to perform the classification of the augmented leaf dataset. Discussion: We proposed a five Layer CNNet (FLCNNet) to classify the leaf image data into different classes or labels based on the shape characteristics of the leaves.

scholarly journals Recognition of 3D Shapes Based on 3V-DepthPano CNN

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Junjie Yin ◽  
Ningning Huang ◽  
Jing Tang ◽  
Meie Fang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Shape Descriptor ◽  
Recognition System ◽  
Fine Tuning ◽  
Training Time ◽  
3D Shape ◽  
3D Shapes ◽  
And Training

This paper proposes a convolutional neural network (CNN) with three branches based on the three-view drawing principle and depth panorama for 3D shape recognition. The three-view drawing principle provides three key views of a 3D shape. A depth panorama contains the complete 2.5D information of each view. 3V-DepthPano CNN is a CNN system with three branches designed for depth panoramas generated from the three key views. This recognition system, i.e., 3V-DepthPano CNN, applies a three-branch convolutional neural network to aggregate the 3D shape depth panorama information into a more compact 3D shape descriptor to implement the classification of 3D shapes. Furthermore, we adopt a fine-tuning technique on 3V-DepthPano CNN and extract shape features to facilitate the retrieval of 3D shapes. The proposed method implements a good tradeoff state between higher accuracy and training time. Experiments show that the proposed 3V-DepthPano CNN with 3 views obtains approximate accuracy to MVCNN with 12/80 views. But the 3V-DepthPano CNN frame takes much shorter time to obtain depth panoramas and train the network than MVCNN. It is superior to all other existing advanced methods for both classification and shape retrieval.

A vision-based active learning convolutional neural network model for concrete surface crack detection

2020 ◽  
Vol 23 (13) ◽  
pp. 2952-2964
Author(s):  
Zhen Wang ◽  
Guoshan Xu ◽  
Yong Ding ◽  
Bin Wu ◽  
Guoyu Lu
Keyword(s):  
Neural Network ◽  
Active Learning ◽  
Convolutional Neural Network ◽  
Surface Crack ◽  
Crack Detection ◽  
Concrete Surface ◽  
Learning Ability ◽  
Training Method ◽  
And Training ◽  
New Framework

Concrete surface crack detection based on computer vision, specifically via a convolutional neural network, has drawn increasing attention for replacing manual visual inspection of bridges and buildings. This article proposes a new framework for this task and a sampling and training method based on active learning to treat class imbalances. In particular, the new framework includes a clear definition of two categories of samples, a relevant sliding window technique, data augmentation and annotation methods. The advantages of this framework are that data integrity can be ensured and a very large amount of annotation work can be saved. Training datasets generated with the proposed sampling and training method not only are representative of the original dataset but also highlight samples that are highly complex, yet informative. Based on the proposed framework and sampling and training strategy, AlexNet is re-tuned, validated, tested and compared with an existing network. The investigation revealed outstanding performances of the proposed framework in terms of the detection accuracy, precision and F1 measure due to its nonlinear learning ability, training dataset integrity and active learning strategy.

scholarly journals Classification of Image using Convolutional Neural Network (CNN)

2019 ◽  
pp. 13-18 ◽  
Author(s):  
Md. Anwar Hossain ◽  
Md. Shahriar Alam Sajib
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Lower Layer ◽  
Classification Systems ◽  
Automatic Extraction ◽  
Discriminative Power ◽  
Single Image ◽  
Training Time ◽  
Discriminant Power

Computer vision is concerned with the automatic extraction, analysis, and understanding of useful information from a single image or a sequence of images. We have used Convolutional Neural Networks (CNN) in automatic image classification systems. In most cases, we utilize the features from the top layer of the CNN for classification; however, those features may not contain enough useful information to predict an image correctly. In some cases, features from the lower layer carry more discriminative power than those from the top. Therefore, applying features from a specific layer only to classification seems to be a process that does not utilize learned CNN’s potential discriminant power to its full extent. Because of this property we are in need of fusion of features from multiple layers. We want to create a model with multiple layers that will be able to recognize and classify the images. We want to complete our model by using the concepts of Convolutional Neural Network and CIFAR-10 dataset. Moreover, we will show how MatConvNet can be used to implement our model with CPU training as well as less training time. The objective of our work is to learn and practically apply the concepts of Convolutional Neural Network.

scholarly journals Classification of surface water objects in visible spectrum images

2020 ◽  
pp. 87-95
Author(s):  
A. A. Artemyev ◽  
E. A. Kazachkov ◽  
S. N. Matyugin ◽  
V. V. Sharonov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Surface Water ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Visible Spectrum ◽  
Training Dataset ◽  
And Training ◽  
Selection Of

This paper considers the problem of classifying surface water objects, e.g. ships of different classes, in visible spectrum images using convolutional neural networks. A technique for forming a database of images of surface water objects and a special training dataset for creating a classification are presented. A method for forming and training of a convolutional neural network is described. The dependence of the probability of correct recognition on the number and variants of the selection of specific classes of surface water objects is analysed. The results of recognizing different sets of classes are presented.

scholarly journals Batik Classification using Deep Learning

2019 ◽  
Vol 8 (4) ◽  
pp. 11416-11421
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Data Analysis ◽  
Convolutional Neural Network ◽  
Image Data ◽  
High Accuracy ◽  
Learning Method ◽  
Philosophy Of Life ◽  
Proposed Model

Batik is one of the Indonesian cultural heritages that has been recognized by the global community. Indonesian batik has a vast diversity in motifs that illustrate the philosophy of life, the ancestral heritage and also reflects the origin of batik itself. Because of the manybatik motifs, problems arise in determining the type of batik itself. Therefore, we need a classification method that can classify various batik motifs automatically based on the batik images. The technique of image classification that is used widely now is deep learning method. This technique has been proven of its capacity in identifying images in high accuracy. Architecture that is widely used for the image data analysis is Convolutional Neural Network (CNN) because this architecture is able to detect and recognize objects in an image. This workproposes to use the method of CNN and VGG architecture that have been modified to overcome the problems of classification of the batik motifs. Experiments of using 2.448 batik images from 5 classes of batik motifs showed that the proposed model has successfully achieved an accuracy of 96.30%.

scholarly journals Images Classification of Gurumukhi Month’s Name Images using Various Convolutional Neural Network Optimizers

2021 ◽  
Author(s):  
Tajinder Pal Singh ◽  
Sheifali Gupta ◽  
Meenu Garg ◽  
Deepika Koundal ◽  
Atef Zaguia
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Complex Structure ◽  
Holistic Approach ◽  
Neural Model ◽  
Text Recognition ◽  
Computational Time ◽  
Dense Layer ◽  
Automatic Feature Extraction

Abstract The Gurumukhi script has a complex structure for which text recognition based on an analytical approach can misinterpret the script. For error-free results in text recognition, the author has proposed a holistic approach based on classification of Gurumukhi month’s name images. For this, a new convolutional neural model has been developed for automatic feature extraction from Gurumukhi text images. The proposed convolutional neural network is designed with five convolutional, three polling layers, one flatten layer and one dense layer. To validate the results of the proposed model, the dataset was self-created from 500 distinct writers. The performance of the model has been analyzed with 100 epochs, 40 batch sizes and different optimizers. The various optimizers that have been used for this experimentation are SGD, Adagrad, Adadelta, RMSprop, Adam, and Nadam. The experimental results show that the proposed CNN model performed best with Adam optimizer in terms of accuracy, computational time, F1 score, precision and recall.

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 An Improved Convolutional Neural Network for Text Classification

2021 ◽  
Vol 2066 (1) ◽  
pp. 012091
Author(s):  
Xiaojing Fan ◽  
A Runa ◽  
Zhili Pei ◽  
Mingyang Jiang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Text Classification ◽  
Classification Accuracy ◽  
Classification Performance ◽  
Classification Model ◽  
Classification Problems ◽  
Training Time ◽  
Accuracy And Precision ◽  
And Training

Abstract This paper studies the text classification based on deep learning. Aiming at the problem of over fitting and training time consuming of CNN text classification model, a SDCNN model is constructed based on sparse dropout convolutional neural network. Experimental results show that, compared with CNN, SDCNN further improves the classification performance of the model, and its classification accuracy and precision can reach 98.96% and 85.61%, respectively, indicating that SDCNN has more advantages in text classification problems.

scholarly journals Fine-Tuned Convolutional Neural Network for Different Cardiac View Classification

2021 ◽  
Author(s):  
Santosh Kumar B P ◽  
Mohd Anul Haq ◽  
Sreenivasulu P ◽  
Siva D ◽  
Malik bader alazzam ◽  
...  
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Convolutional Neural Network ◽  
Cardiac Disease ◽  
Classification Performance ◽  
Computational Techniques ◽  
Ultrasound Images ◽  
The Us ◽  
And Training

Abstract In echocardiography, an electrocardiogram is conventionally utilized in the chronological arrangement of diverse cardiac views for measuring critical measurements. Cardiac view classification plays a significant role in the identification anddiagnosis of cardiac disease. Early detection of cardiac disease can be cured or treated, and medical experts accomplish this. Computational techniques classify the views without any assistance from medical experts. The process of learning and training faces issues in feature selection, training and classification. Considering these drawbacks, an effective rank-based deep convolutional neural network (R-DCNN) for the proficient feature selection and classification of diverse views of ultrasound images (US). Significant features in the US image are retrieved using rank-based feature selectionand used to classify views. R-DCNN attains 96.7% classification accuracy, and classification results are compared with the existing techniques. From the observation of the classification performance, the R-DCNN outperforms the existing state-of-art classification techniques.

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