Learning For Image Recognition Research Articles

Machine Learning-Assisted Precision Manufacturing of Atom Qubits in Silicon.

Donor-based qubits in silicon, manufactured using scanning tunneling microscope (STM) lithography, provide a promising route to realizing full-scale quantum computing architectures. This is due to the precision of donor placement, long coherence times, and scalability of the silicon material platform. The properties of multiatom quantum dot qubits, however, depend on the exact number and location of the donor atoms within the quantum dots. In this work, we develop machine learning techniques that allow accurate and real-time prediction of the donor number at the qubit site during STM patterning. Machine learning image recognition is used to determine the probability distribution of donor numbers at the qubit site directly from STM images during device manufacturing. Models in excess of 90% accuracy are found to be consistently achieved by mitigating overfitting through reduced model complexity, image preprocessing, data augmentation, and examination of the intermediate layers of the convolutional neural networks. The results presented in this paper constitute an important milestone in automating the manufacture of atom-based qubits for computation and sensing applications.

Constructing a cataplexy face prediction model for narcolepsy type 1 based on ResNet-18

Objective: To establish a prediction model for the identifying of cataplexy facial features based on clinical shooting videos by using a deep learning image recognition network ResNet-18. Methods: A cross-sectional study. Twenty-five narcolepsy type 1 patients who were first diagnosed and never received treatment and 25 healthy controls recruited by advertisement in the Second Affiliated Hospital of Nanchang University from 2020 to 2023.After image preprocessing, a total of 1 180 images were obtained, including 583 cataplexy faces and 597 normal faces.90% were selected as the training set and validation set, and then expanded the data by 5 times.80% of the expanded data set was extracted as the training set and 20% as the validation set, that is, the number of the training set was (583+597)×0.9×0.8×5=4 248, the number of the validation set was (583+597)×0.9×0.2×5=1 062. The data sets for training and validation were used train parameters to establish the model and were trained through the five-fold cross-validation method, to establish the ResNet-18 cataplexy face recognition model via transfer learning.10% (118 images) of the original non-amplified images were extracted as the test set. The test set data did not participate in data enhancement and model training, and was only used to evaluate the final performance of the model. Finally, ResNet-18 was compared with VGG-16, ResNet-34 and Inception V3 deep learning models, and the receiver operating characteristic curve was used to evaluate the value of ResNet-18 image recognition network in cataplexy face recognition. Results: Among 25 patients with narcolepsy type 1, 15 were males and 10 were females, aged [M (Q1, Q3)] of 14.0(11.0, 20.5) years.Among 25 healthy controls, 14 were males and 11 were females, with a median age of 16.0(14.4, 23.0) years.The overall accuracy of ResNet-18 image recognition network in the test set was 90.9%, the sensitivity was 96.4% and the specificity was 85.2%. The area under the ROC curve was 0.99(95%CI:0.96-1.00). The ResNet-18 model parameter amount was 11.69 M, the floating point operation amount was 1 824.03 M, and the single image recognition time was 5.9 ms. Conclusions: The cataplexy face prediction model built based on the deep learning image recognition network ResNet-18 has a high accuracy in identifying cataplexy faces.

Classification of pain expression images in elderly with hip fractures based on improved ResNet50 network.

The aim of this study is designed an improved ResNet 50 network to achieve automatic classification model for pain expressions by elderly patients with hip fractures. This study built a dataset by combining the advantages of deep learning in image recognition, using a hybrid of the Multi-Task Cascaded Convolutional Neural Networks (MTCNN). Based on ResNet50 network framework utilized transfer learning to implement model function. This study performed the hyperparameters by Bayesian optimization in the learning process. This study calculated intraclass correlation between visual analog scale scores provided by clinicians independently and those provided by pain expression evaluation assistant(PEEA). The automatic pain expression recognition model in elderly patients with hip fractures, which constructed using the algorithm. The accuracy achieved 99.6% on the training set, 98.7% on the validation set, and 98.2% on the test set. The substantial kappa coefficient of 0.683 confirmed the efficacy of PEEA in clinic. This study demonstrates that the improved ResNet50 network can be used to construct an automatic pain expression recognition model for elderly patients with hip fractures, which has higher accuracy.

Enhancing Agricultural Efficiency: Deep Learning-Based Soil Crack Detection for Water Irrigation

The escalating demand for agricultural precision and environmental monitoring underscores the necessity for effective soil crack detection methods. This study explores the feasibility of employing a Raspberry Pi-powered camera system and deep learning image recognition to detect soil cracks and control agricultural irrigation. The purpose is to develop a soil crack detection system using deep learning techniques, sustain plant growth process, increase productivity, and optimize water irrigation practice. Our approach leverages TensorFlow to craft a convolutional neural network tailored specifically for execution on a Raspberry Pi 3B+. A dataset comprises manually captured images and is trained with the InceptionV3 model categorized into crack or nocrack classes. The accuracy is achieved ranging from 97% to 99%. These results underscore deep learning image recognition models on Raspberry Pi for cost-effective soil crack monitoring and controlling the plants watering system.

Research on the generation and annotation method of thin section images of tight oil reservoir based on deep learning

The cast thin sections of tight oil reservoirs contain important parameters such as rock mineral composition and content, porosity, permeability and stratigraphic characteristics, which are of great significance for reservoir evaluation. The use of deep learning technology for intelligent identification of thin section images is a development trend of mineral identification. However, the difficulty of making cast thin sections, the complexity of the making process and the high cost of thin section annotation have led to a lack of cast thin section images, which cannot meet the training requirements of deep learning image recognition models. In order to increase the sample size and improve the training effect of deep learning model, we proposed a generation and annotation method of thin section images of tight oil reservoir based on deep learning, by taking Fuyu reservoir in Sanzhao Sag as the target area. Firstly, the Augmentor strategy space was used to preliminarily augment the original images while preserving the original image features to meet the requirements of the model. Secondly, the category attention mechanism was added to the original StyleGAN network to avoid the influence of the uneven number of components in thin sections on the quality of the generated images. Then, the SALM annotation module was designed to achieve semi-automatic annotation of the generated images. Finally, experiments on image sharpness, distortion, standard accuracy and annotation efficiency were designed to verify the advantages of the method in image quality and annotation efficiency.

Research on Intelligent System of Multimodal Deep Learning in Image Recognition

In this paper, a multi-scale image estimation method based on wavelet transform is proposed, which can effectively remove motion features from multiple videos. Then the autoencoder with sparsity limit is used to adjust the input signal to achieve effective compression. The effective features are extracted and the optimal unique vector is learned. The improved convolutional neural network is used to recognize weak moving objects. Experiments show that the algorithm can achieve high accuracy without large-scale learning samples, and the highest recognition rate is 99.36%. This algorithm has a great improvement over conventional algorithm.

Analysing Deep Learning and Machine learning Model for Cattle Recognition

In machine learning, feature extraction is a very important step in the construction of any pattern classification that extracts relevant features to identify the class from group of images. To recognizing object, accuracy depends upon the quality of features extracted from an image. Unique feature extraction has high accuracy in recognizing classes. In Deep learning image recognition is based on such as strong feature extraction ability and high recognition accuracy. In this paper we have discussed all the approaches used to recognize cattle from traditional to deep learning approaches, we have also analyzed the comparison of machine and deep learning approaches. We tried to explore few models of deep learning and its architecture that may result with high accuracy.

Performance of convolutional neural networks on CIFAR and Dog Breed classification dataset

This research aims to study the deep learning applications in image recognition and classification tasks. The advantages and limitations of deep learning are explored by analysing existing deep learning algorithms and their applications on tasks such as using images to do classification, image segmentation, and target detection. In the experimental part, this paper evaluates the performance of deep learning in image recognition by using classical neural network models, including training and testing models on a large size of image datasets. The results show that neural network has good classification and detection capabilities in image recognition tasks and also achieves good results for image segmentation tasks. However, the model training process of shallow neural network models is time-consuming and performs poorly for small-scale datasets. Based on these situations, this paper proposes some optimization strategies to achieve high performance and efficiency of neural networks in image recognition and analyze the performance differences between different strategies .

The recognition and analysis of fake images based on deep neural network

The generation of fake images and the process of technology is developing quickly, which has generated great concern about its impact on society. To effectively identify the fake image, this article introduces a deep learning-based identification method. This article uses a convolutional neural network (CNN) neural network to construct a model for machine learning image recognition and lets it learn a dataset of hundreds of real and fake pictures to let it complete tasks that are difficult for humans to complete. the use of deep learning to solve this problem has the following advantages: the results can be observed after data input, which is conservative and fast, and there is no need to manually design rules as Deep learning methods can optimise the loss function to learn the rules, mining potential features of data, with strong representation ability. Using this method, the accuracy result is over 60%. This paper proves a machine can learn and recognise real and fake pictures, which is specifically inspiring that learning-based methods can also solve the challenging problem of images.

Application of Neural Networks and Machine Learning in Image Recognition

Application of Neural Networks and Machine Learning in Image Recognition

A kick monitoring method for deepwater open-circuit drilling based on convolutional neural network

In the case of shallow drilling in deepwater without installation of riser and subsea BOP, open-circuit drilling is usually used. In the process of open-circuit drilling, drilling fluid is directly returned to the seabed. Therefore, observing the return of seabed drilling fluid through Remotely Operated Vehicle (ROV) has become one of the main methods for monitoring the kick in open-circuit drilling. However, the current ROV monitoring method only relies on the professional technical personnel to observe the video screen, which is difficult to identify the kick quickly and accurately in the early stage. In view of this problem, an intelligent kick monitoring method based on deep learning image recognition technology is proposed in this paper. By collecting the actual video image data shot by ROV and simulating the drilling fluid return morphology under different gas influx using Ansys Fluent software, a typical sample database is constructed. Then, the pretrained GoogLeNet neural network model is used for transfer learning, and the video images shot by ROV on site are used to test the trained model. The results show that this method can identify the kick more quickly and accurately, and the model has good robustness. Using this method can greatly reduce the economic losses in the process of deepwater open-circuit drilling, and provides technical support for safe and efficient drilling of deepwater open-circuit drilling.

Thrips Detection and Monitoring System in Mango Greenhouses Based on Deep Learning Image Recognition

Thrips Detection and Monitoring System in Mango Greenhouses Based on Deep Learning Image Recognition

Road surface damages allocation with RTI-IMS software based on YOLO V5 model

ABSTRACT Research on applying artificial intelligence to autonomously detect road surface damages contributes positively to road traffic maintenance and management. This study introduces the Road Traffic Infrastructure Intelligent Management System (RTI IMS) software, utilizing the Yolo V5 programming platform. This software is designed to automatically identify road surface damages during operation. Based on machine learning image recognition technology, the model is trained with a diverse database of road surface damage images from Japan, India, and Vietnam. Additionally, to improve the inference efficiency of the RTI IMS software, the study uses the Mosaic method, tripling the variation to diversify input data during training, thereby minimizing prediction errors. The research results in the successful development of the RTI IMS software, capable of autonomously detecting road surface damages and providing corresponding images to traffic management authorities. This study significantly contributes to the modernization of current road traffic management practices.

Fault detection and diagnosis of energy system based on deep learning image recognition model under the condition of imbalanced samples

Faults in energy systems impact the reliability of the energy supply and cause energy waste. Data-driven fault detection and diagnosis (FDD) methods can detect and diagnose system faults by mining historical operational data. However, the quantitative imbalance between fault and normal samples degrades the performance of the FDD method. Therefore, this study proposes a novel deep learning-based FDD method under the condition of imbalanced samples that converts the time-series signals into an image signal to extract the timing and coupling features, and then applies the improved conditional variational autoencoder-generative adversarial network (CVAE-GAN) to generate fault samples for balancing the training sample set. Subsequently, according to the framework of image recognition, a two-dimensional convolutional neural network was adopted to identify the image samples and achieve FDD. The experimental results showed that, under the condition of imbalanced samples, the proposed method could increase diagnosis accuracy by an average of 5.71% compared with other common data-driven methods. After the samples were augmented with the improved CVAE-GAN, the accuracy improved by an average of 3.79%. Consequently, the feasibility and superiority of the proposed method were demonstrated.

딥러닝 영상인식 기술을 이용한 가상/혼합현실 기반 통합 검출 플랫폼 제안

딥러닝 영상인식 기술을 이용한 가상/혼합현실 기반 통합 검출 플랫폼 제안

Deep Learning in Image Recognition

With the rapid development of high-tech in China, the field of artificial intelligence has also been promoted. Image recognition is an important topic in the field of artificial intelligence, which mainly includes two modules: classification recognition and feature extraction; At the same time, as an important research direction of AI, deep learning has made rapid progress in recent years. It has been widely used in image recognition, speech recognition and other fields and has achieved great success. This paper analyzes the application of deep learning in image recognition, mainly from the aspects of face recognition, remote sensing image classification, etc. Its purpose is to provide help for relevant practitioners, so as to promote the development of image recognition in the tide of AI development.

Misfire Detection Technology with Deep Neural Network Based on Ignition Coil Signals

<div>For achieving high efficiency and low exhaust emissions, engines need to be operated near the limits of stable combustion, such as lean or exhaust gas recirculation (EGR) conditions. Sensing technologies of the combustion state by existing engine components are of high interest. And the utilization of voltage and current signals from ignition coils is discussed in this article. The discharge channel of an ignition spark is strongly affected by flow variation and spark plug surface conditions, and the behavior of discharge channel stretching and restrike event can vary greatly from cycle to cycle. As a result, the effects of flow velocity, temperature, pressure, and electrode surface resistance are compounded in the voltage-current response, making it difficult to accurately detect the combustion state for each cycle by a threshold judgment process using a single feature value.</div> <div>In this article, a method for inductively detecting misfires from voltage and current signals of ignition coils by applying deep learning image recognition is introduced. First, post-ignition for misfire detection is performed on the engine bench during the expansion stroke in an engine cycle, when the cylinder pressure is expected to differ between the combustion cycle and the ignition cycle, and the ignition coil voltage and current are measured. Next, a two-dimensional frequency distribution of voltage and current (discharge histogram) is created as an input image for deep learning, and the AlexNet model, which has been trained with more than one million images, is trained with images of the ignition and combustion cycles as a supervised learning. The accuracy of classification is then verified using a validation dataset. In addition, to making the deep learning model more explainable, the activation score distribution on the discharge histogram was visualized when the trained model judges the images, and the discharge characteristics that provided the basis for deep learning classifications were analyzed.</div>

Sustainable strategy for online physical education teaching using ResNet34 and big data.

Since the global COVID-19 outbreak in the spring of 2020, online instruction has replaced traditional classroom instruction as the main method of educating students. Teaching physical education online can be challenging, as it may be difficult to teach students certain movements, accurate student mobility, and appropriate exercise assignments. This paper proposed an online teaching support system with sustainable development features that utilize several large data sets. The system is based on the deep learning image recognition algorithm ResNet34, which can analyze and correct student actions in real-time for gymnastics, dance, basketball, and other sports. By combining the attention mechanism module with the original ResNet34, the detection precision of the system can be enhanced. The sustainability of the system is evident from the fact that the data set can be expanded in response to the emergence of new sports categories and can be kept current in real-time. According to experiments, the target identification accuracy of the proposed system, which combines ResNet34 and the attention mechanism, is higher than that of several other methods currently in use. The proposed techniques outperform the original ResNet34 in terms of accuracy, precision, and recall by 4.1%, 2.8%, and 3.6%, respectively. The suggested approach significantly improves student action correction in virtual sports instruction.

Building an application for automatic identification image result of Kirby-Bauer disk diffusion susceptibility test

Objectives: AI (artificial intelligence) technologies have allowed us to automate the analysis, processing, and decision-making based on vast amounts of medical data. As a result, disease diagnosis has become more accurate and faster, saving time and enhancing treatment capabilities. Building upon this, we conducted a study on the application of machine learning in image recognition of Kirby-Bauer diffusion antibiotic susceptibility test results to lay the foundation for further research in the use of artificial intelligence to support healthcare professionals in examination, diagnosis, and decision-making in the medical field. Material and methods: We utilized 200 images of antibiotic susceptibility test results obtained through Kirby-Bauer diffusion technique, with the number of antibiotic zones ≤ 6. The images were labeled to distinguish the inhibition zone and antibiotic zones. We employed the Single-shot Detection (SSD) MobileNet method to achieve a balance between speed, ease of deployment, and accuracy, especially when working with devices with limited computational power. The results obtained from the model’s detection of antibiotic zone labels were used as input for the result recognition algorithm. Results and discussion: Out of the 200 images, 160 (80%) were successfully identified with sufficient information regarding the number of antibiotic zones. Among the 40 failed images, 30 (15%) lacked sufficient recognition of both the bacterial inhibition zones and the number of antibiotic zones, while 10 (5%) images were unrecognizable. The application demonstrated accurate identification and classification of antibiotic zone positions and bacterial inhibition circles in images with appropriate brightness and contrast. Conclusion: We have successfully developed an initial tool and algorithm based on a machine learning model to quickly and accurately determine the necessary information of a Kirby-Bauer antibiotic susceptibility test result, including the number of antibiotic zones, bacterial inhibition circles, and diameter of inhibition circles, achieving an accuracy rate of 80%. Key words: deep learning, disk diffusion susceptibility test, Kirby-Bauer, image recognition, artifical intelligence.

A Novel Method for Recognizing Space Radiation Sources Based on Multi-Scale Residual Prototype Learning Network

As a basic task and key link of space situational awareness, space target recognition has become crucial in threat analysis, communication reconnaissance and electronic countermeasures. Using the fingerprint features carried by the electromagnetic signal to recognize is an effective method. Because traditional radiation source recognition technologies are difficult to obtain satisfactory expert features, automatic feature extraction methods based on deep learning have become popular. Although many deep learning schemes have been proposed, most of them are only used to solve the inter-class separable problem and ignore the intra-class compactness. In addition, the openness of the real space may invalidate the existing closed-set recognition methods. In order to solve the above problems, inspired by the application of prototype learning in image recognition, we propose a novel method for recognizing space radiation sources based on a multi-scale residual prototype learning network (MSRPLNet). The method can be used for both the closed- and open-set recognition of space radiation sources. Furthermore, we also design a joint decision algorithm for an open-set recognition task to identify unknown radiation sources. To verify the effectiveness and reliability of the proposed method, we built a set of satellite signal observation and receiving systems in a real external environment and collected eight Iridium signals. The experimental results show that the accuracy of our proposed method can reach 98.34% and 91.04% for the closed- and open-set recognition of eight Iridium targets, respectively. Compared to similar research works, our method has obvious advantages.