Research on Recognition and Classification Technology Based on Deep Convolutional Neural Network

Image recognition and classification using a convolution neural network is an important application of image processing. How too reasonably to design the convolutional layer of the convolutional neural network, the number of hidden layers and optimize the parameters of the convolutional neural network to ensure high accuracy and efficiency in image recognition and classification are an extremely important part. The core of this thesis is the model design and parameter optimization of deep convolutional neural networks. This paper mainly designs, implements, optimizes and adjusts the model structure of the convolutional network of Tensor Flow framework platform. We redesigned the convolutional neural network model to a depth of 19 layers and used two data sets for training, testing and parameter optimization. Experimental results show that the convolution neural network models presented in this paper is superior to other neural network models in accuracy and efficiency of image recognition and classification, and has a good guiding role in solving practical engineering problems.

In recent years Neural Networks and especially Deep Neural Networks (DNN) have seen a rise in popularity. DNNs have increased the overall performance of algorithms in applications such as image recognition and classification, 2D and 3D pose detection, natural language processing, and time series forecasting. Especially in image classification and recognition, so-called Convolutional Neural Network (CNN) have gained high interest as they can reach a high accuracy, which makes them a viable solution in cancer detection or autonomous driving. As CNNs are widely used in image tasks, different visualizations techniques have bee developed to show how their internals are working. Apart from image tasks, CNNs are applicable to other problems, e.g., time series classification, time series forecasting, or natural language processing. CNNs in those contexts behave similarly, allowing for the same visualization techniques to make them more interpretable. In this chapter, we adapt image visualization algorithms to time series problems, allowing us to build granular, intuitively interpretable feature hierarchies to make a time series forecast as understandable as an image recognition task. We do so by using our previous work on power time series forecasting using CNN Auto Encoders (AEs) and applying typical CNN visualization techniques to it. Thus, we guid computer scientists to provide better interpretable figures for a time series forecasting task to application domain experts.KeywordsVisualizationConvolutional neural networksTime series data

With the development of big data information and success in computer vision problems, more hidden layers in CNNs give it a greater and complicated structure and more powerful characteristic. Convolutional Neural Networks (CNN) provide an opportunity for automatically gaining knowledge of the domain specific features. The convolutional neural network is model and skilled by means of the deep leaning of neural networks and the set of rules has made great achievements in computer vision considering the fact that it’s a creation. This paper first explains the upward push and structure of deep learning and convolution neural network (CNN), and summarizes the structure or shape of CNN, and its different operations like convolution, feature extraction and pooling operation of convolution neural network. Development of convolution neural network model primarily based on deep learning in image classification are reviewed, an intensive literature survey of Convolution Neural Networks which is the broadly used framework of deep learning. With Alex Net or ImageNet because the base model of image classification in CNN model, we've got reviewed all the versions emerged over the years to fit various programs and a small discussion on structure and working of CNN.

The recent advancement of big data technology causes the data from agriculture domain to enter into the big data. They are not conventional techniques in existence to process such a large volume of data. The processing of large datasets involves parallel computation and analysis model. Hence, it is necessary to use big data analytics framework to process a large image datasets. In this paper, an automated big data framework is presented to classify the plant disease condition. This framework consists of a series operations that leads into a final step. When the classification is carried out using novel image classifier. The image classifier is designed using a Convolutional Recurrent Neural Network Classifier (CRNN) algorithm. The classifier is designed in such a way that it provides classification between a normal leaf and an abnormal leaf. The classification of plant images over large datasets that includes banana plant, pepper, potato, and tomato plant. Which is compared with other existing big data plant classification techniques like convolutional neural network, recurrent neural network, and deep neural network, artificial neural network with forward and backward propagation. The result shows that the proposed method obtains improved detection and classification of diseased plants compared to other the convolutional neural network (94.14%), recurrent neural network (94.07%), deep neural network (94%), artificial neural network with forward (93.96%), and backward propagation method (93.66%).

<p>As an important research achievement in the field of brain like computing, deep convolution neural network has been widely used in many fields such as computer vision, natural language processing, information retrieval, speech recognition, semantic understanding and so on. It has set off a wave of neural network research in industry and academia and promoted the development of artificial intelligence. At present, the deep convolution neural network mainly simulates the complex hierarchical cognitive laws of the human brain by increasing the number of layers of the network, using a larger training data set, and improving the network structure or training learning algorithm of the existing neural network, so as to narrow the gap with the visual system of the human brain and enable the machine to acquire the capability of "abstract concepts". Deep convolution neural network has achieved great success in many computer vision tasks such as image classification, target detection, face recognition, pedestrian recognition, etc. Firstly, this paper reviews the development history of convolutional neural networks. Then, the working principle of the deep convolution neural network is analyzed in detail. Then, this paper mainly introduces the representative achievements of convolution neural network from the following two aspects, and shows the improvement effect of various technical methods on image classification accuracy through examples. From the aspect of adding network layers, the structures of classical convolutional neural networks such as AlexNet, ZF-Net, VGG, GoogLeNet and ResNet are discussed and analyzed. From the aspect of increasing the size of data set, the difficulties of manually adding labeled samples and the effect of using data amplification technology on improving the performance of neural network are introduced. This paper focuses on the latest research progress of convolution neural network in image classification and face recognition. Finally, the problems and challenges to be solved in future brain-like intelligence research based on deep convolution neural network are proposed.</p>

<p><b>Image classification problems often face the issues of high dimensionality and large variance within the same class. Deep convolutional neural networks are designed to solve the problem by extracting features using convolutional operations. Researchers have developed complex deep convolutional neural networks to achieve the outstanding performance that outperforms humans. However, the complexity of deep convolutional neural networks brings two side effects. First, the more complex the network architecture is, the harder it is to design. Second, it deteriorates the black-box nature of deep convolutional neural networks, which is harder to explain. To tackle the above two issues, neural architecture search and explainable deep learning have emerged as two promising research areas for automatically designing deep convolutional neural networks and providing explanations of the predictions made by deep learning models, respectively. Evolutionary computation based neural architecture search has been employed to automatically design deep convolutional neural networks that outperform those manually designed, but the computational cost is too high. Surrogate-assisted and transfer learning based methods can be utilised to reduce the computational cost. Furthermore, a branch in explainable deep learning called local approximation does not need machine learning expertise to understand the explanation. The target of local approximation is to find interpretable features. Evolutionary computation has successfully applied to many search problems, but it has never been used in finding local approximation. </b></p> <p>The overall goal of this thesis is to improve the efficiency of evolutionary neural architecture search in image classification tasks and provide explanations in the inference phase. This can mitigate the major issues -- the difficulty of designing deep convolutional neural networks and the lack of explainability, that significantly affected deep learning being widely used in real-world applications. </p> <p>This thesis proposes a surrogate-assisted particle swarm optimisation (an evolutionary computation algorithm) method to efficiently evolve deep convolutional neural networks. A surrogate model is proposed to predict a better solution from a pair of solutions, and a method for sampling a subset of the dataset as a surrogate dataset is proposed to reduce the computational cost to less than 3 GPU-days, but with very competitive classification accuracies across several benchmark datasets. </p> <p>Next, this thesis proposes to use smaller datasets in multiple source domains to evolve deep convolutional neural networks, and then transfer the learned models to the target domain. This has achieved the goal of accelerating the evolutionary neural architecture search process and improving the generalisation performance, which is supported by the experiment results. </p> <p>Furthermore, this thesis proposes a genetic algorithm (an evolutionary computation algorithm) based method to evolve local explanations to explain the predictions of deep convolutional neural networks. By combining the flexible encoding and the proposed fitness evaluation, the proposed method can efficiently evolve meaning interpretable features in the local explanation. It produces competitive explanations, but 10 times faster than Local Interpretable Model-agnostic Explanations (LIME) -- the state-of-the-art method. </p> <p>Lastly, an evolutionary multi-objective approach is explored to evolve local explanations to reduce the human effort of examining the interpretable features in the local explanations. It adds the second objective of minimising the number of interpretable features, which reduces the human effort in checking them. Additionally, it proposes a method to select only two non-dominated solutions from the pareto front to save the labour cost for end users.</p>

Convolutional Neural Networks (CNNs) have garnered significant utilisation within automated image classification systems. CNNs possess the ability to leverage the spatial and temporal correlations inherent in a dataset. This study delves into the use of cutting-edge deep learning for precise image data classification, focusing on overcoming the difficulties brought on by the COVID-19 pandemic. In order to improve the accuracy and robustness of COVID-19 image classification, the study introduces a novel methodology that combines the strength of Deep Convolutional Neural Networks (DCNNs) and Generative Adversarial Networks (GANs). This proposed study helps to mitigate the lack of labelled coronavirus (COVID-19) images, which has been a standard limitation in related research, and improves the model’s ability to distinguish between COVID-19-related patterns and healthy lung images. The study uses a thorough case study and uses a sizable dataset of chest X-ray images covering COVID-19 cases, other respiratory conditions, and healthy lung conditions. The integrated model outperforms conventional DCNN-based techniques in terms of classification accuracy after being trained on this dataset. To address the issues of an unbalanced dataset, GAN will produce synthetic pictures and extract deep features from every image. A thorough understanding of the model’s performance in real-world scenarios is also provided by the study’s meticulous evaluation of the model’s performance using a variety of metrics, including accuracy, precision, recall, and F1-score.

With the progress of artificial intelligence, technology based on deep learning is becoming more and more mature, and the application of deep convolutional neural network for image classification has become a popular topic for researchers. The number of the structure of deep convolutional neural network for image classification is keep increasing, and its performance is consistently improving, gradually replace that of traditional methods. According to the process of model development and model optimization, this paper divides the convolutional neural network into two models: classical deep convolutional neural network model and attention mechanism deep convolutional neural network model. The construction methods and characteristics of various kinds of deep convolutional neural network models are comprehensively reviewed, and the performance of various classification models is compared and analyzed. Finally, the problems of deep convolutional neural network model are presented.

Deep convolutional neural networks have achieved great success in recent years. They have been widely used in various applications such as optical and SAR image scene classification, object detection and recognition, semantic segmentation, and change detection. However, deep neural networks rely on large-scale high-quality training data, and can only guarantee good performance when the training and test data are independently sampled from the same distribution. Deep convolutional neural networks are found to be vulnerable to subtle adversarial perturbations. This adversarial vulnerability prevents the deployment of deep neural networks in security-sensitive applications such as medical, surveillance, autonomous driving and military scenarios. This paper first presents a holistic view of security issues for deep convolutional neural network-based image recognition systems. The entire information processing chain is analyzed regarding safety and security risks. In particular, poisoning attacks and evasion attacks on deep convolutional neural networks are analyzed in detail. The root causes of adversarial vulnerabilities of deep recognition models are also discussed. Then, we give a formal definition of adversarial robustness and present a comprehensive review of adversarial attacks, adversarial defense, and adversarial robustness evaluation. Rather than listing existing research, we focus on the threat models for the adversarial attack and defense arms race. We perform a detailed analysis of several representative adversarial attacks on SAR image recognition models and provide an example of adversarial robustness evaluation. Finally, several open questions are discussed regarding recent research progress from our workgroup. This paper can be further used as a reference to develop more robust deep neural network-based image recognition models in dynamic adversarial scenarios.

The report presents an algorithm for constructing a convolutional hierarchical neural network classifier, which is a modification of the algorithm for constructing hierarchical neural network classifiers suggested before. The original algorithm was designed to exploit intrinsic class hierarchy to build a class tree with a neural network in each node classifying groups of initial classes (in a non-terminal node) or a subset of original classes (in a terminal node). The convolutional modification utilizes convolutional neural networks instead of regular fully connected networks in order to apply the model to image classification tasks. Use of class hierarchy for image classification should reduce the number of adjusted neural network parameters compared to deep convolutional neural networks, and therefore it should reduce training and inference time. In this context the algorithm may be compared with some pruning techniques. The convolutional hierarchical neural network classifier inherits some hyperparameters of a conventional hierarchical neural network classifier, like the activation threshold and the threshold by the share of voting patterns. The goal of this study was to explore different strategies of choosing these hyperparameters. To test these strategies, we used the CIFAR-10 dataset. Also, for demonstration purposes we apply the convolutional hierarchical neural network classifier to the CIFAR-100 dataset.

Based on deep convolutional neural network, an optical remote sensing image classification method is proposed in this paper. Aiming at the particularity of remote sensing image and natural object classification, combined with the theory of deep learning convolutional neural network, a five-layer convolutional neural network was designed, which applied to classify the optical remote sensing image into two category. Testing and parameter optimization on the UC Merced Land Use data set. The convolutional neural network designed in this paper is trained and tested on the same test set. The result shows it has better effect of classifying on the current data set reach 98.15%. The experimental results indicate this network designed can apply to the scene of two-category image classification and improve the classification accuracy of aerial image.

Deep learning algorithms based on convolutional neural networks have been widely researched and developed in the field of images. This helps in more accurate classification and recognition of images. In order to improve the recognition accuracy of convolutional neural network and optimize the learning performance of neural network, an improved dynamic adaptive pooling algorithm is proposed. First, an overview of the basic structure of convolutional neural networks, convolutional layers and pooling layer operations. Second, build a convolutional neural network model, study and compare different network pooling models. Finally, an improved dynamic adaptive pooling model is constructed for the case where the existing algorithm has a slow convergence speed. Experiment on handwritten database. The simulation results show that as the number of iterations continues to increase, the mean square error continues to decrease, and the recognition accuracy of the model continues to improve. The improved pooling method not only makes the feature extraction of the convolutional neural network more accurate, but also improves the convergence speed and model accuracy, and achieves the purpose of optimizing the network learning performance. This approach can be further extended to other models related to convolutional neural networks.

The impressive gain in performance obtained using deep neural networks (DNN) for various tasks encouraged us to apply DNN for image classification task. We have used a variant of DNN called Deep convolutional Neural Networks (DCNN) for feature extraction and image classification. Neural networks can be used for classification as well as for feature extraction. Our whole work can be better seen as two different tasks. In the first task, DCNN is used for feature extraction and classification task. In the second task, features are extracted using DCNN and then SVM, a shallow classifier, is used to classify the extracted features. Performance of these tasks is compared. Various configurations ofDCNNare used for our experimental studies.Among different architectures that we have considered, the architecture with 3 levels of convolutional and pooling layers, followed by a fully connected output layer is used for feature extraction. In task 1 DCNN extracted features are fed to a 2 hidden layer neural network for classification. In task 2 SVM is used to classify the features extracted by DCNN. Experimental studies show that the performance of υ-SVM classification on DCNN features is slightly better than the results of neural network classification on DCNN extracted features.

Identification of crops is an important topic in the agricultural domain. Hyperspectral remote sensing data are very useful for crop feature extraction and classification. Remote sensing data is an unstructured data and Convolutional Neural Network (CNN) can work on unstructured data efficiently. This paper presents an evaluation of CNN for crop classification using the Indian Pines standard dataset obtained from the AVIRIS sensor and the study area dataset obtained from the EO-1hyperion sensor. Optimized CNN has been tuned by training the model on different parameters. It has been compared with two classification algorithms: Deep Neural Network (DNN) and Convolutional Autoencoder. According to the test results, the proposed optimized CNN model provided better results as compared to the other two methods. CNN has given 97 ± 0.58% overall accuracy for the Indian Pines standard dataset and 78 ± 2.43% for our study area dataset.

Deep learning based on structured deep neural networks has provided powerful applications in various fields. The structures imposed on the deep neural networks are crucial, which makes deep learning essentially different from classical schemes based on fully connected neural networks. One of the commonly used deep neural network structures is generated by convolutions. The produced deep learning algorithms form the family of deep convolutional neural networks. Despite of their power in some practical domains, little is known about the mathematical foundation of deep convolutional neural networks such as universality of approximation. In this paper, we propose a family of new structured deep neural networks: deep distributed convolutional neural networks. We show that these deep neural networks have the same order of computational complexity as the deep convolutional neural networks, and we prove their universality of approximation. Some ideas of our analysis are from ridge approximation, wavelets, and learning theory.