Application of deep learning in image processing of unmanned vehicles

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.

In recent years, the integration of machine learning (ML) techniques with image processing has revolutionized various industries, from healthcare and automotive to entertainment and security. The ability to analyze and interpret visual data with unprecedented accuracy and speed has opened new frontiers in artificial intelligence (AI), leading to transformative applications and innovations.Image processing, traditionally a domain of signal processing, has evolved significantly with the advent of machine learning. Classical image processing methods, which relied heavily on manually crafted features and heuristic algorithms, often struggled with the complexity and variability inherent in real-world images. Machine learning, particularly deep learning, has addressed these limitations by automatically learning hierarchical representations of data, enabling more robust and accurate image analysis.The impact of machine learning-based image processing is profound. In healthcare, for example, ML algorithms can assist in diagnosing diseases from medical images with a precision comparable to or surpassing that of human experts. In autonomous vehicles, advanced image processing enables real-time recognition of objects and environments, ensuring safe navigation. Additionally, in security, facial recognition systems have become more reliable, enhancing surveillance capabilities.Despite its significant advantages, the integration of machine learning in image processing presents challenges, including the need for large datasets, computational resources, and the potential for bias in algorithmic decision-making. Moreover, the ethical and privacy implications of deploying these technologies, especially in sensitive areas like healthcare and surveillance, warrant careful consideration.This paper aims to explore the multifaceted impact of machine learning on image processing, highlighting the technological advancements, practical applications, and associated challenges. By examining the current state of the art and future directions, this research seeks to provide a comprehensive understanding of how machine learning is reshaping the landscape of image processing and its implications for various sectors.

The paper is concerned with the visions and threats of Artificial Intelligence. It characterizes and describes those that, according to experts and scientists , will have a huge impact on the shape, nature and functioning of societies in the next two to three decades. Undoubtedly, one of the significant challenges is the development and application of deep learning. Scientific research in Artificial Intelligence will be accompanied by intensive development of computer vision, convolutional networks and virtual, augmented and mixed reality. In the future, everything digital will be able to fall prey to counterfeiters. The essence and functioning of related Deepfake tools are explained and described in the paper. The development of Artificial Intelligence will result in a wide and versatile application of Biometrics. The paper describes the prospective areas of its use and draws attention to the problems associated with the construction of the oversized databases necessary for this purpose. In the era of a society characteristic of the development of the fourth industrial revolution, theway and means of automobile transportation and the necessary infrastructure will be radically transformed - autonomous vehicles will appear. The paper describes all levels of reference relating to the scope of control of an autonomous vehicle. Effective use of Artificial Intelligence solutions will not be possible without computers with enormous computing power. Theemergence of quantum computers will certainly solve this problem. The essence of quantum computer functioning is described in the next chapter. Artificial Intelligence can successfully perform many tasks better than humans. This will make Artificial Intelligence take on a huge economic value. Positions will be lost for both physical and mental workers. The question then becomes, where are we? Which professions can take over Artificial Intelligence and which ones will not move? What is the future of work done by humans? These issues are addressed in Chapter 9. The prospect of widespread and comprehensive use of Artificial Intelligence solutions will raise the following question, with respect to both humans and society. Can Artificial Intelligence optimize our happiness? The answer to such a question is not an easy one. The paper attempts to answer it. In the age of Robotics and Artificial Intelligence, we will be accompanied by, revolutionary changes in the way humans of entire societies function. There will be an urgent need to define a neweconomic model. Issues clarifying the nature and importance of the essential components of the said model are addressed in this work. Solutions and use of Artificial Intelligence are often accompanied by various kinds of failures. These can have many causes. The paper describes and explains, proposed by Yampolsky Roman, a kind of dysfunction pattern. The results of scientific research andpractical experience that the list of Artificial Intelligence dangers is very extensive. A representative list of risks from Artificial, along with the characteristics of each of them, is presented in the last part of the paper.

In recent years, Deep Learning (DL), such as the algorithms of Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Generative Adversarial Networks (GAN), has been widely studied and applied in various fields including agriculture. Researchers in the fields of agriculture often use software frameworks without sufficiently examining the ideas and mechanisms of a technique. This article provides a concise summary of major DL algorithms, including concepts, limitations, implementation, training processes, and example codes, to help researchers in agriculture to gain a holistic picture of major DL techniques quickly. Research on DL applications in agriculture is summarized and analyzed, and future opportunities are discussed in this paper, which is expected to help researchers in agriculture to better understand DL algorithms and learn major DL techniques quickly, and further to facilitate data analysis, enhance related research in agriculture, and thus promote DL applications effectively. Keywords: deep learning, smart agriculture, neural network, convolutional neural networks, recurrent neural networks, generative adversarial networks, artificial intelligence, image processing, pattern recognition DOI: 10.25165/j.ijabe.20181104.4475 Citation: Zhu N Y, Liu X, Liu Z Q, Hu K, Wang Y K, Tan J L, et al. Deep learning for smart agriculture: Concepts, tools, applications, and opportunities. Int J Agric & Biol Eng, 2018; 11(4): 32-44.

Artificial Intelligence: The Future of Maxillofacial Prognosis and Diagnosis?

This chapter systematically summarizes the principles, methods and applications of deep learning. First, the basic principles such as deep feedforward network, back propagation, regularization, optimization methods, hyperparameters, loss functions, parameter initialization strategy, and overfitting and underfitting, and the paradigms of small and zero sample, transfer, multi-task, multi-label, and multi-output learning were presented. Then, a detailed introduction to mainstream deep learning algorithms was shown, such as deep belief network, auto-encoder, convolutional neural network (CNN), deep CNN, fully convolutional network, and Recurrent neural network. Finally, the applications of deep learning, such as classification, detection, and segmentation, and some related public datasets were described.

In today’s digital environment, the pattern is everywhere. It is present in many aspects of our daily life. Algorithms can be used to detect or mathematically observe a pattern physically. Vector feature values represent a pattern in the digital world. With the advent of artificial intelligence (AI) techniques in the recent era, there are so many machine learning (ML) and deep learning (DL) models that have been developed. ML is the branch of AI, which can perform tasks like data analysis, analytical model building, and classification without being explicitly programmed. DL is the subset of ML in AI. In DL, mostly artificial neural networks (ANNs) are utilized. It has the capability of work based upon unsupervised learning from the data that is unlabeled and unstructured. Using these DL and ML models, extract the meaningful features from the given image or video is known as pattern recognition (PR). PR is used in many engineering applications such as computer vision, natural language processing, character recognition, robotics, speech recognition, and so on. It is also used in many medical image processing applications and telemedicine. The present work discussed the pattern recognition problem and its various stages in detail. In addition to that, the application of deep learning and machine learning in pattern recognition is also explained briefly.

Nowadays, machine learning (ML) has attained a high level of achievement in many contexts. Considering the significance of ML in medical and bioinformatics owing to its accuracy, many investigators discussed multiple solutions for developing the function of medical and bioinformatics challenges using deep learning (DL) techniques. The importance of DL in Internet of Things (IoT)-based bio- and medical informatics lies in its ability to analyze and interpret large amounts of complex and diverse data in real time, providing insights that can improve healthcare outcomes and increase efficiency in the healthcare industry. Several applications of DL in IoT-based bio- and medical informatics include diagnosis, treatment recommendation, clinical decision support, image analysis, wearable monitoring, and drug discovery. The review aims to comprehensively evaluate and synthesize the existing body of the literature on applying deep learning in the intersection of the IoT with bio- and medical informatics. In this paper, we categorized the most cutting-edge DL solutions for medical and bioinformatics issues into five categories based on the DL technique utilized: convolutional neural network, recurrent neural network, generative adversarial network, multilayer perception, and hybrid methods. A systematic literature review was applied to study each one in terms of effective properties, like the main idea, benefits, drawbacks, methods, simulation environment, and datasets. After that, cutting-edge research on DL approaches and applications for bioinformatics concerns was emphasized. In addition, several challenges that contributed to DL implementation for medical and bioinformatics have been addressed, which are predicted to motivate more studies to develop medical and bioinformatics research progressively. According to the findings, most articles are evaluated using features like accuracy, sensitivity, specificity, F-score, latency, adaptability, and scalability.

The rapid advancement of deep learning has significantly transformed the development of next-generation autonomous vehicles, enhancing both safety and efficiency. This paper explores the integration of deep learning techniques, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and reinforcement learning, in perception, decision-making, and control systems of autonomous vehicles. By leveraging vast datasets and real-time processing, deep learning enables precise object detection, path planning, and adaptive driving strategies. Furthermore, the implementation of sensor fusion techniques combining LiDAR, radar, and cameras enhances situational awareness, reducing the risk of accidents. Despite these advancements, challenges such as computational complexity, adversarial robustness, and ethical considerations remain key research areas. This study provides an overview of the current state-of-the-art deep learning applications in autonomous vehicles and discusses future directions toward fully autonomous, safer, and more efficient transportation systems.

Few-shot learning aims to obtain a better deep learning model through training with a smaller amount of data. In recent years, with the development and application of deep learning, deep learning has faced the problem of few-shot in many scenarios. Therefore, the study of few-shot learning will promote the application and deployment of deep learning in the industrial field. First of all, this survey specifically introduces two few-shot learning implementation methods: based on data augmentation and based on transfer learning. Then, this survey lists actual cases to illustrate the application of few-shot learning in industry. One case is about Few-shot parts surface defect detection based on metric learning. Another case is about poultry egg detection in few-shot based on meta learning. Finally, this survey summarizes the research achievements and research value of few-shot learning. Survey summarizes the problems and challenges faced by few-shot learning and the future development direction of few-shot learning.KeywordsFew-shot learningData augmentationTransfer learningMetric learningMeta learning

Deep learning is a complex machine learning algorithm that can learn the deep inner rules of a sample. In recent years, deep learning has achieved good results in the field of image processing. The essence of deep learning is to build a multi-layered machine learning architecture that trains on large-scale data. With the development of computer technology, deep learning has achieved good results in image recognition and natural language processing. Deep learning is also becoming more and more widely used in industry. This paper introduces the applications of deep learning in the industrial field, and finally discusses development and challenges of deep learning in industrial applications in the future.

ObjectivesThis study aimed to investigate the accuracy of deep learning algorithms to diagnose tooth caries and classify the extension and location of dental caries in cone beam computed tomography (CBCT) images. To the best of our knowledge, this is the first study to evaluate the application of deep learning for dental caries in CBCT images. MethodsThe CBCT image dataset comprised 382 molar teeth with caries and 403 noncarious molar cases. The dataset was divided into a development set for training and validation and test set. Three images were obtained for each case, including axial, sagittal, and coronal. The test dataset was provided to a multiple-input convolutional neural network (CNN). The network made predictions regarding the presence or absence of dental decay and classified the lesions according to their depths and types for the provided samples. Accuracy, sensitivity, specificity, and F1 score values were measured for dental caries detection and classification. ResultsThe diagnostic accuracy, sensitivity, specificity, and F1 score for caries detection in carious molar teeth were 95.3%, 92.1%, 96.3%, and 93.2%, respectively, and for noncarious molar teeth were 94.8%, 94.3%, 95.8%, and 94.6%. The CNN network showed high sensitivity, specificity, and accuracy in classifying caries extensions and locations. ConclusionsThis research demonstrates that deep learning models can accurately identify dental caries and classify their depths and types with high accuracy, sensitivity, and specificity. The successful application of deep learning in this field will undoubtedly assist dental practitioners and patients in improving diagnostic and treatment planning in dentistry. Clinical significance: This study showed that deep learning can accurately detect and classify dental caries. Deep learning can provide dental caries detection accurately. Considering the shortage of dentists in certain areas, using CNNs can lead to broader geographic coverage in detecting dental caries.

Deep Learning and Beyond: AI’s New Horizons” is an expansive monograph that delves into the dynamic and rapidly evolving field of deep learning, a key area of artificial intelligence. This comprehensive work provides an insightful exploration of the revolutionary developments, foundational concepts, and future directions of deep learning, positioning itself as a crucial resource for understanding this transformative technology. The monograph commences with an introductory chapter that lays the groundwork by elucidating the background of machine learning and chronicling the ascent of deep learning. This foundational perspective is vital for comprehending the underlying principles and methodologies that have propelled deep learning to the forefront of technological innovation. Subsequent chapters delve into the “Historical Evolution” of AI, tracing the origins of neural networks from their early inception to the Renaissance period of deep learning. This historical analysis is pivotal in understanding the field’s trajectory and significant milestones. The “Foundations of Deep Learning” chapter presents an in-depth analysis of neural networks, covering their basic architecture, activation functions, layers, and the critical process of backpropagation and gradient descent. This is followed by examining “Advanced Neural Network Architectures,” where the focus shifts to state-of-the-art models such as convolutional neural networks, recurrent neural networks, and groundbreaking transformers and attention mechanisms. In addressing the “Training Deep Models,” the monograph explores regularization techniques, optimization strategies, and the increasingly significant role of transfer learning and pre-trained models. The application-oriented chapter, “Applications of Deep Learning,” showcases real-world implementations in domains like image recognition, natural language viii processing, and autonomous systems, illustrating deep learning technologies’ vast potential and versatility. The monograph also tackles the “Challenges in Deep Learning,” including overfitting, interpretability, and the ethical considerations surrounding AI deployment. It then ventures into the “Future of Deep Learning,” contemplating emerging trends such as integrating quantum computing, neural architecture search, and the convergence of neuroscience and deep learning. “Case Studies” provide practical insights and empirical evidence of the impact of deep learning. At the same time, the concluding chapter synthesizes the current state of deep learning, offers predictions for the future, and provides reflective thoughts on this transformative field. “Deep Learning and Beyond: AI’s New Horizons” educates and informs readers about the vast potential and future possibilities of deep learning and artificial intelligence. Keywords: Deep Learning, Artificial Intelligence, Neural Networks, Machine Learning, Convolutional Neural Networks, Recurrent Neural Networks, Transformers, Quantum Computing, Ethical AI, Neural Architecture Search, Natural Language Processing, Autonomous Systems, AI in Healthcare, Future of AI, AI Applications, AI Challenges.

Along with the development of medical imaging technology, medical image analysis has become a crucial part of clinical diagnosis. However, traditional image analysis methods rely on manual feature extraction and expert evaluation, triggering problems such as low efficiency and lack of accuracy. Deep learning techniques, especially convolutional neural networks (CNNs), have made significant breakthroughs in medical image analysis in recent years. The aim of this paper is to study the application of deep learning in medical imaging, and explore the advantages and development potential in early disease diagnosis, image segmentation and feature extraction. This paper reviews the basic principles of deep learning and analyses the applicability of deep learning in medical imaging by combining the characteristics of medical imaging. Through systematic analysis of existing literature, this paper summarises the remarkable achievements of deep learning in processing, analysing and applying it to various medical images, such as Optical Coherence Tomography (OCT), Magnetic Resonance Imaging (MRI) and so on. It has been shown that deep learning methods can significantly improve the accuracy and efficiency of medical image processing and demonstrate performance due to traditional methods in tasks such as classification. However, how to improve the interpretability of models and the generalisability of clinical applications are still current research hotspots and challenges.

Background: Artificial intelligence (AI) and deep learning (DL) are rapidly changing the field of diagnostics and imaging in cardiology, offering tools for automatic segmentation, quantification of changes, and risk stratification. These technologies have the potential to increase diagnostic accuracy, work efficiency, and individualization of patient care. Methods: This structured narrative review critically evaluates clinically validated applications of artificial intelligence (AI) and deep learning (DL) in cardiovascular medicine, focusing on imaging (echocardiography, coronary CT angiography, cardiac MRI, and ECG), risk stratification, and biomarker integration. A systematic literature search was conducted in PubMed for studies published between January 2015 and December 2026, supplemented by references from key articles. Original English-language studies reporting quantitative clinical outcomes were included, with 78 studies ultimately analyzed. Results: AI and DL models, including convolutional neural networks and transformers, achieved performance comparable to experts in cardiac imaging, myocardial perfusion assessment, valve defect detection, and coronary event prediction. Multimodal approaches improved diagnostic accuracy and reproducibility, while explainable AI enhanced transparency and clinical confidence. Deep learning also enabled faster image acquisition and processing without compromising precision. Conclusions: AI and DL have transformative potential in cardiology, offering fast, accurate, and scalable diagnostic tools. The integration of multimodal data, the validation of algorithms in prospective studies, and ensuring the transparency of models are key. Future research should focus on prospective, multicenter validations and the ethical and safe implementation of AI in everyday clinical practice.