Dishes Recognition System Based on Deep Learning

With the development of artificial intelligence technology, food image recognition has become an important research direction in the field of computer vision. The region of Northern Shaanxi is famous for its rich food culture. This paper aims to propose a food image recognition method based on an improved ResNet network to enhance the recognition rate of characteristic foods in Northern Shaanxi. Firstly, the principles and structure of basic convolutional neural networks (CNNs) were introduced, with a focus on the application and optimization design of CNNs in food image recognition. This mainly included AC blocks fused with asymmetric convolutions, attention modules based on improving food image recognition performance, and residual structure design for enhancing learning effectiveness. Secondly, the FoodResNet18 model was constructed with a specially designed enhancement block and a deep, shallow shared attention residual module to enhance the feature extraction ability and perception of visual information by the model. To improve the generalization ability of the model, this paper comprehensively preprocessed the self-built Northern Shaanxi Food-300 dataset, covering the sources of data, processing methods, and data augmentation strategies used for training. The model training and comparative analysis show that the food image recognition method based on improved ResNet outperforms traditional CNN models in multiple experiments. In the ablation experiment, the specific contribution of the design module to the final recognition performance was analyzed, and the advantages of the deep shallow shared attention residual module in feature extraction and preservation were verified.

Worldwide, in 2014, more than 1.9 billion adults, 18 years and older, were overweight. Of these, over 600 million were obese. Accurately documenting dietary caloric intake is crucial to manage weight loss, but also presents challenges because most of the current methods for dietary assessment must rely on memory to recall foods eaten. The ultimate goal of our research is to develop computer-aided technical solutions to enhance and improve the accuracy of current measurements of dietary intake. Our proposed system in this paper aims to improve the accuracy of dietary assessment by analyzing the food images captured by mobile devices (e.g., smartphone). The key technique innovation in this paper is the deep learning-based food image recognition algorithms. Substantial research has demonstrated that digital imaging accurately estimates dietary intake in many environments and it has many advantages over other methods. However, how to derive the food information (e.g., food type and portion size) from food image effectively and efficiently remains a challenging and open research problem. We propose a new Convolutional Neural Network (CNN)-based food image recognition algorithm to address this problem. We applied our proposed approach to two real-world food image data sets (UEC-256 and Food-101) and achieved impressive results. To the best of our knowledge, these results outperformed all other reported work using these two data sets. Our experiments have demonstrated that the proposed approach is a promising solution for addressing the food image recognition problem. Our future work includes further improving the performance of the algorithms and integrating our system into a real-world mobile and cloud computing-based system to enhance the accuracy of current measurements of dietary intake.

The recognition of food images is of great significance for nutrition monitoring, food retrieval and food recommendation. However, the accuracy of recognition had not been high enough due to the complex background of food images and the characteristics of small inter-class differences and large intra-class differences. To solve these problems, this paper proposed a food image recognition method based on transfer learning and ensemble learning. Firstly, generic image features were extracted by using the convolutional neural network models (VGG19, ResNet50, MobileNet V2, AlexNet) pre-trained on the ImageNet dataset. Secondly, the 4 pre-trained models were transferred to the food image dataset for model fine-tuning. Finally, different basic learner combination strategies were adopted to establish the ensemble model and classify feature information. In this paper, several kinds of experiments were performed to compare the results of food image recognition between single models and ensemble models on food-11 dataset. The experimental results demonstrated that the accuracy of the ensemble model was the highest, reaching 96.88%, which was superior to any base learner. Therefore, the convolutional neural network model based on transfer learning and ensemble learning has strong learning ability and generalization ability, and it is feasible and practical to apply the method to food image recognition.

Food image recognition is very useful in nowadays applications. It has been shown recently that the recognition accuracy depends on the ratio between background and foreground of the food image. In this work, the food image recognition using ResNet with FOOD-101 dataset is studied. The food area in the image is determined using ENet. The effect of the increase of the ratio between background and foreground of food images in terms of recognition accuracy is determined. It is found that increasing such ratio from the inner food area of the image can help increasing the accuracy. An average of 2.5% for increasing the accuracy is obtained when increasing the ratio of 100% from the inner ratio. Additionally, a higher accuracy can also be achieved compared to the overall full image recognition accuracy, for the case of inner food area ratio with 50% increasing ratio. These results show that with a different food image input, it is possible to increase the food image recognition accuracy.

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.

With the rapid development of technology, artificial intelligence is gradually applied in the field of image processing. Traditional image processing and recognition takes human extraction of features as a means to describe and characterize low-level edge information in images, but a large number of features extraction causes certain troubles to image recognition, and it is difficult to extract deep-level information. In recent years, deep learning, as an important emerging technology in the field of image recognition, has broad application prospects, and the research on image recognition technology has important theoretical value and practical significance to promote the development of computer vision and artificial intelligence. It opens up a whole new direction in image processing research, thus meeting the demand for efficient image processing. This paper reviews the applications of deep learning in image recognition, analyzes the classical convolutional neural network model, and summarizes the applications of deep learning in the fields of license plate recognition, face recognition, and medical image recognition.

Aiming at the problem of objects in image recognition rectangle, but objects which are input into convolutional neural networks square, the object recognition model was put forward which was based on BING method to realize object estimate, used vectorization of convolutional neural networks to realize input square image in convolutional networks, therefore, built joint convolution neural networks, which achieve multiple size image input. Verified by experiments, the accuracy of multi-object image recognition was improved by 6.70% compared with single vectorization of convolutional neural networks. Therefore, image recognition method of joint convolutional neural networks can enhance the accuracy in image recognition, especially for target in rectangular shape.

Convolutional Neural Network (CNN) is a deep learning algorithm that is widely used to identify and classify a digital image object. In this study the Convolutional Neural Network (CNN) is used as an algorithm that functions to identify leaf types (certain plants) based on images obtained from a public dataset provider named Daun Jamu Indonesia. The existence of image characteristics causes the assistance process to require a more detailed feature selection process. Therefore the CNN method is used in order to solve the problem. The Convolutional Neural Network (CNN) method is capable of performing image recognition by minimizing feature extraction. CNN is also reliable in processing unstructured data because it uses a multi-layered structure of artificial reasoning networks. The image recognition process is carried out by looking for the shape of the model that matches the processed data in order to get the best results. In this study, the augmentation process was carried out on the training data and validation data so that overfitting does not occur in the Convolutional Neural Network (CNN). The results obtained in this study indicate that the Convolutional Neural Network (CNN) method can identify leaf types with a measured accuracy rate of 92% using the Confusion Matrix evaluation method. It is hoped that this research can be used as a reference for the use of the Convolutional Neural Network (CNN) method for image data, especially plant leaf types.

Deep learning has been the core topic in machine learning and convolutional neural network is one of the most prominent approaches. Convolutional neural network has won numerous competitions in recent years. It has outstanding results in image recognition. We review the different deep learning approaches which have been used in the field of image classification and localization.

With the rapid development of digital image processing and recognition, the intelligent grid wins a great opportunity. We could be able to get the information from digital images quickly based on computer analysis, algorithms of deep learning and machine learning, grid data extraction, model training and image recognition. The article firstly reviewed the current status of traditional grid, including a broad overview of the specific strengths and weaknesses, secondly analyzed the intelligence algorithms of deep learning and machine learning based on neural network, then achieved the recognition and reconstruction of wiring diagram through image recognition based on neural network, lastly showed the prospects of image recognition in power system.

Text recognition in natural scene images is a challenging problem in computer vision. Different than the optical character recognition (OCR), text recognition in natural scene images is more complex due to variations in text size, colors, fonts, orientations, complex backgrounds, occlusion, illuminations and uneven lighting conditions. In this paper, we propose a segmentation-free method based on a deep convolutional recurrent neural network to solve the problem of cursive text recognition, particularly focusing on Urdu text in natural scenes. Compared to the non-cursive scripts, Urdu text recognition is more complex due to variations in the writing styles, several shapes of the same character, connected text, ligature overlapping, stretched, diagonal and condensed text. The proposed model gets a whole word image as an input without pre-segmenting into individual characters, and then transforms into the sequence of the relevant features. Our model is based on three components: a deep convolutional neural network (CNN) with shortcut connections to extract and encode the features, a recurrent neural network (RNN) to decode the convolutional features, and a connectionist temporal classification (CTC) to map the predicted sequences into the target labels. To increase the text recognition accuracy further, we explore deeper CNN architectures like VGG-16, VGG-19, ResNet-18 and ResNet-34 to extract more appropriate Urdu text features, and compare the recognition results. To conduct the experiments, a new large-scale benchmark dataset of cropped Urdu word images in natural scenes is developed. The experimental results show that the proposed deep CRNN network with shortcut connections outperform than other network architectures. The dataset is publicly available and can be downloaded from <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://data.mendeley.com/datasets/k5fz57zd9z/1</uri> .

Image recognition is one of the most important fields of image processing and computer vision. Food image classification is an unique branch of image recognition problem. In modern days people are more conscious about their health. A system that can classify food from image is necessary for a dietary assessment system. Classification of food images is very challenging since the dataset of food images is highly non-linear. In this paper we proposed a method that can classify food categories with images. We used convolutional neural network to classify food images. The CNNs are a very effective class of neural networks that is highly effective at the task of image classifying., object detection and other computer vision problems. We classified a food dataset consisting different food categories with 16643 images. We obtained an accuracy of 92.86% in our experiment.

Introduction. Working with graphic images is an essential element of almost any modern computer-aided design systems. The result of neural network, including deep image processing, can be recognition of the belonging classes of objects present on them. Objects of the real world require large expenditures for the development and implementation of highly specialized computer vision systems. In recent years, there has been an improvement of the quality characteristics obtained in the field of technical vision. This is made possible by artificial neural networks. The article deals with the recognition of a flat, black and white flowchart image. These are two-dimensional images or their selected parts, which are displayed in an arbitrary graphic format by system means on a computer monitor screen. Basic block diagram shapes: rectangle, rhombus, parallelogram, circle, ellipse (oval), etc. The purpose of the article is to solve the problem of graphic image recognition. The systems for the recognition of graphic images include modern systems of computer-aided design, management and document management. The article has formed a basic set of training and test images of block diagram nodes. Neural network models are proposed to improve the detection accuracy of block diagram nodes based on fully connected and convolutional neural networks. Results. The basic procedures of the block diagram image recognition algorithm were partially tested at the software level, and allow us to conclude the effectiveness of the proposed structural methods. A comparative analysis of neural network and syntactic structural approaches for solving this problem is carried out. Conclusions. Two methods of recognizing flat graphic figures and recognizing connections between figures in flowcharts are proposed: a method of recursive traversal of all branches of the tree of the current union of connections between figures and the figures themselves, and also a study was carried out for the created neural network in PyTorch to solve this problem using trained neural network methods. Keywords: image recognition, convolutional neural networks, syntactic analysis.

Synthetic aperture radar (SAR) image target detection and recognition (SAR-TDR) tasks have become research hot spots in the remote sensing application. These targets include ships, vehicles, aircraft, oil tanks, bridges, and so on. However, with the rapid development of SAR technology and increasingly complex electromagnetic environment, complex characteristics of SAR images bring severe challenges to the accurate SAR-TDR via traditional physical models or manual feature-extraction-based machine learning methods. In recent years, deep learning (DL), as a powerful automatic feature extraction algorithm, has been widely used in the computer vision domain. More specifically, DL has also been introduced into the SAR-TDR tasks and has effectively achieved good performance in terms of accuracy, real-time processing, etc. With the rapid development of DL, SAR image processing, and practical requirements of SAR-TDR in civilian and military domains, it is crucial to conduct a systematic survey on SAR-TDR in the past few years. In this survey article, we mainly conduct a systematic overview of DL-based SAR-TDR literature on two tasks, i.e., target recognition (e.g., ground vehicles, ships, and aircraft) and target detection (e.g., ships, aircraft, change detection, sea surface oil spills, and oil tanks). More specifically, our related works about these topics are also presented to verify the effectiveness of DL-based methods. First, several DL methods (e.g., convolutional neural networks, recurrent neural networks, autoencoders, and generative adversarial networks), commonly used in SAR-TDR, are briefly introduced. Then, a systematic review of DL-based SAR-TDR (including our related works) is presented. Finally, the current challenges and future possible research directions are deeply analyzed and discussed.

Small sample image recognition using improved Convolutional Neural Network