RC-sized Autonomous vehicle with Prediction Model

Artificial Intelligence in the American Healthcare Industry: Looking Forward to 2030

Computer vision is an essential field of artificial intelligence (AI). Under the scope of this field, the software is made to perceive the data through cameras, pictures, and videos. Although computers do not have a biological vision of their own, they are still very capable of processing an image by themselves. Under the scope of computer vision, numeric values are used as features to train machine learning models that can make actual predictions about an image and its contents. Scientists often use a combination of machine learning models and PaaS solutions, such as the cognitive services offered by Microsoft Azure, to bridge the gap between what a person sees and what a computer can do with it. The beginning of the chapter gives an overview of Azure Cognitive Services and why you should use them. In this section, you will learn about Azure Cognitive Services such as speech, language, vision, decision, and the OpenAI service. Following that, you will learn about computer vision and its key components, such as image classification and object detection. Following that, you will learn about the various computer vision applications and services that Azure supports, such as computer vision, custom vision, and face and form recognizers.

Computer vision (CV) is a subfield of artificial intelligence (AI) that trains computers to understand our visual world based on digital images. There are many successful applications of CV including face and hand gesture detection, weather recording, smart farming, and self-driving cars. Recent advances in computer vision with machine learning (ML) provide a new avenue for microbiological analysis. ML has been proven efficient and precise due to its automatic analysis of the geometric features and texture of organisms in optical microscopy images. This work describes a workshop that teaches basics of CV using ML (image classification, object detection) using Python notebook examples in Google Colab and Jetson Nano. The workshop is designed for senior undergraduate or beginning graduate chemistry students who would like to learn how to classify and detect microorganisms such as amoebae. The workshop content includes three sections: image classification with convolutional neutral netowork (CNN) in Google Colab, object detection with Mask R-CNN in Google Colab, and object detection with SSD-mobilenet in Jetson Nano.

Object detection is a hot topic with various applications in computer vision, e.g., image understanding, autonomous driving, and video surveillance. Much of the progresses have been driven by the availability of object detection benchmark datasets, including PASCAL VOC, ImageNet, and MS COCO. However, object detection on the drone platform is still a challenging task, due to various factors such as view point change, occlusion, and scales. To narrow the gap between current object detection performance and the real-world requirements, we organized the Vision Meets Drone (VisDrone2018) Object Detection in Image challenge in conjunction with the 15th European Conference on Computer Vision (ECCV 2018). Specifically, we release a large-scale drone-based dataset, including 8, 599 images (6, 471 for training, 548 for validation, and 1, 580 for testing) with rich annotations, including object bounding boxes, object categories, occlusion, truncation ratios, etc. Featuring a diverse real-world scenarios, the dataset was collected using various drone models, in different scenarios (across 14 different cities spanned over thousands of kilometres), and under various weather and lighting conditions. We mainly focus on ten object categories in object detection, i.e., pedestrian, person, car, van, bus, truck, motor, bicycle, awning-tricycle, and tricycle. Some rarely occurring special vehicles (e.g., machineshop truck, forklift truck, and tanker) are ignored in evaluation. The dataset is extremely challenging due to various factors, including large scale and pose variations, occlusion, and clutter background. We present the evaluation protocol of the VisDrone-DET2018 challenge and the comparison results of 38 detectors on the released dataset, which are publicly available on the challenge website: http://www.aiskyeye.com/. We expect the challenge to largely boost the research and development in object detection in images on drone platforms.

Object detection is a hot topic in the field of machine vision. In the innovation and development of modern technology, the integration of machine learning, pattern recognition, image processing and other multidisciplinary knowledge can effectively solve the image problem. In computer vision and digital image processing technology in the development of innovation, target detection theory research and practical application has achieved excellent results, although there are many problems need to solve, but a lot of new technology and new method has yet to be developed, so countries scholars gradually strengthen the scientific research strength, strong adaptability are put forword technique, high precision and good robustness. In this paper, based on the understanding of the research status of object detection, according to the basic theory of image processing, the application of computer vision in object detection is analyzed. The final experimental results show that shadow detection, image enhancement and filtering methods can effectively improve the application effect.

Computer vision is a subdomain of artificial intelligence that permits computers to extract useful info from digital images and videos. Artificial Intelligence enables to think, but in Computer Vision enable to see, observe and understand Computer Vision includes many techniques to solve real time problems. This research work focuses on one among these techniques viz. object detection. A computer vision technology called object detection helps locate and identify objects in images. In this paper mainly focusing on Neural network-based object detection techniques and how these techniques will impact on real time applications.

InceptB: A CNN Based Classification Approach for Recognizing Traditional Bengali Games

Computer vision is one of the hottest research fields in deep learning. The emergence of generative adversarial networks (GANs) provides a new method and model for computer vision. The idea of GANs using the game training method is superior to traditional machine learning algorithms in terms of feature learning and image generation. GANs are widely used not only in image generation and style transfer but also in the text, voice, video processing, and other fields. However, there are still some problems with GANs, such as model collapse and uncontrollable training. This paper deeply reviews the theoretical basis of GANs and surveys some recently developed GAN models, in comparison with traditional GAN models. The applications of GANs in computer vision include data enhancement, domain transfer, high-quality sample generation, and image restoration. The latest research progress of GANs in artificial intelligence (AI) based security attack and defense is introduced. The future development of GANs in computer vision is also discussed at the end of the paper with possible applications of AI in computer vision.

This survey provides a comprehensive overview of recent advancements and challenges in Artificial Intelligence (AI)‐enabled computer vision (CV) techniques for space robotic missions, spanning critical phases such as Entry, Descent, and Landing (EDL), orbital operations, and planetary surface exploration. Emphasis is placed on deep‐learning–based approaches for image classification, object detection, semantic segmentation, relative pose estimation, and feature matching. State‐of‐the‐art methods in terrain‐relative navigation, crater‐based or rock‐feature matching, and pose estimation for uncooperative targets are highlighted, illustrating the progress achieved through hybrid pipelines combining deep neural networks with classical geometry. The paper also critically evaluates publicly available orbital and planetary data sets—along with the increasing role of synthetic data—for developing and benchmarking CV algorithms under strict resource limitations and harsh environmental conditions. Despite demonstrated success in tasks like autonomous landing, debris removal, and rover navigation, current solutions face significant hurdles. These include computational constraints on onboard hardware, insufficient coverage of planetary conditions in existing data sets, and limited adaptability to dynamically changing environments. To address these shortcomings, research must prioritize lightweight neural architectures, advanced synthetic data generation, adaptive or incremental learning, and robust multisensor fusion. By integrating these strategies, AI‐based CV systems can advance autonomy, precision, and resilience in future space missions.

Today, Computer Vision is one of the most well-liked and critical research areas in the Computer Science field. Moving object detection is the first and foremost step in any computer vision applications. The detection of moving object in any computer vision application is a very critical task. The most widely used method for moving object detection is Frame differencing based background subtraction method. The most of the researchers address the various challenges of background subtraction algorithm to detect the object accurately, the segmentation of objects from non stationary objects is critical, adaptation of illumination changes and handling of camouflage and etc. But they are more CPU and I/O intensive one. Cloud Computing is the new buzz word in computer field which provides computational power on demand basis which requires the representation of e-science workflow model for such kind of large-scale scientific applications. There are four different types of scientific work flow models viz Montage, LIGO, SIPHT, and Cybershake are exist which are varied by either CPU intensive or I/O intensive application. But the Temporal frame differencing based real time object detection is both CPU and I/O intensive one. Hence which could not be expresses as a single workflow model for cloud computing. So, we proposed a hybrid workflow model (Montage and SIPHT) of temporal frame differencing algorithm for real time object detection and the performance of such workflow model is tested and analyzed in already existing scheduling algorithm in workflowsim.

n the last decade, there has been an active digitalization of industrial production based on rapidly developing information technologies, including artificial intelligence technologies. This is largely due to the development of deep learning methods and their applications in computer vision. Since the mid 2010s convolutional neural networks demonstrate exceptional efficiency in solving problems such as the detection, classification and segmentation of various objects. As a result, computer vision methods are beginning to be actively used in the problems of quality control of raw materials and finished products. All this applies to the mining industry. However, in the Russian scientific literature there are practically no systematic reviews of computer vision applications in this area. The present study aims to fill this gap. The paper provides a systematic review of the history of development and the current state of the methods and technologies of machine vision used in the mining industry for the analysis of solid materials, demonstrates the latest achievements in this area and examples of their application in the mining industry. The authors have analyzed 29 research papers in the field of application of computer vision in the mining industry and classified the stages of technology development from the mid-1980s, when computer vision was used without the use of machine learning, and ending with modern research based on the use of deep convolutional neural networks for solving problems of classification and segmentation. The effectiveness of the methods used is compared, their advantages and disadvantages are discussed, and forecasts are made for the development of computer vision methods in the mining industry in the near future. Examples are given showing that the use of convolutional neural networks made it possible to move to a qualitatively higher level of quality in solving problems of classification and segmentation as applied to the analysis of output volume, particle size distribution, including flakiness, angularity and roughness, dust and clay content, bulk density and emptiness, etc.

Graph neural networks (GNNs) is an information - processing system that uses message passing among graph nodes. In recent years, GNN variants including graph attention network (GAT), graph convolutional network (GCN), and graph recurrent network (GRN) have shown revolutionary performance in computer vision applications using deep learning and artificial intelligence. These neural network model extensions, collect information in the form of graphs. GNN may be divided into three groups based on the challenges it solves: link prediction, node classification, graph classification. Machines can differentiate and recognise objects in image and video using standard CNNs. Extensive amount of research work needs to be done before robots can have same visual intuition as humans. GNN architectures, on the other hand, may be used to solve various image categorization and video challenges. The number of GNN applications in computer vision not limited, continues to expand. Human-object interaction, actin understanding, image categorization from a few shots and many more. In this paper use of GNN in image and video understanding, design aspects, architecture, applications and implementation challenges towards computer vision is described. GNN is a strong tool for analysing graph data and is still a relatively active area that needs further researches attention to solve many computer vision applications.

As the rapid advancement and usage of Artificial Intelligence (AI) and the Internet of Things (IoT) have significantly increased the modernization in the autonomous vehicles, converting them from prototypes that are experimental, to a practical solution for next/new generation transportation systems. AI techniques like computer vision, deep learning, and reinforcement learning empower vehicles to perceive their surroundings, make intelligent driving decisions, and adapt to dynamic or extreme road conditions. Wherever IoT ensures flawless communication through Vehicle to Vehicle (V2V), Vehicle to Infrastructure (V2I), and cloud-based networks, enabling the exchange of critical data in real-time. Using these technologies together enhances or improves road safety for individuals, also helps to reduce clogging, maximizes fuel efficiency, and promotes sustainable mobility in urban areas. Moreover, AI and IoT allow/enable the vehicles to support innovations like precision maintenance, intelligent traffic coordination, and intelligent fleet management. Along with these advancements, there are challenges such as the higher cost for advanced technology, cybersecurity risks, governmental policies, and the acceptance of the public, which is very critical to address. The integration of the IoT and AI in upcoming autonomous vehicles showcases a step towards the intelligent transportation system, having the capacity to redefine terms such as mobility, logistics, and planning the urban areas in upcoming decades.

This work presents a perspective of the processes of phytosanitary control in crops, starting with traditional methods and strategies applied for precision agriculture and the use of artificial intelligence and computer vision in this area. Then, the article describes the approach for developing the proposed algorithm based on artificial intelligence and computer vision for phytopathogenic detection. The methodology for the development and validation stages is specifically discussed. Several tests were carried out with the different image processing algorithms studied. Results show how the selected method with Haar filters and Gradient-oriented Histograms performs effectively for identification of phytopathogenic factors, from both qualitative and quantitative analysis.

This paper proposes that Artificial Intelligence (AI) progresses through several overlapping generations: AI 1.0 (Information AI), AI 2.0 (Agentic AI), AI 3.0 (Physical AI), and a speculative AI 4.0 (Conscious AI). Each AI generation is driven by shifting priorities among algorithms, computing power, and data. AI 1.0 accompanied breakthroughs in pattern recognition and information processing, fueling advances in computer vision, natural language processing, and recommendation systems. AI 2.0 is built on these foundations through real-time decision-making in digital environments, leveraging reinforcement learning and adaptive planning for agentic AI applications. AI 3.0 extended intelligence into physical contexts, integrating robotics, autonomous vehicles, and sensor-fused control systems to act in uncertain real-world settings. Building on these developments, the proposed AI 4.0 puts forward the bold vision of self-directed AI capable of setting its own goals, orchestrating complex training regimens, and possibly exhibiting elements of machine consciousness. This paper traces the historical foundations of AI across roughly 70 years, mapping how changes in technological bottlenecks from algorithmic innovation to high-performance computing to specialized data have stimulated each generational leap. It further highlights the ongoing synergies among AI 1.0, 2.0, 3.0, and 4.0, and explores the ethical, regulatory, and philosophical challenges that arise when artificial systems approach (or aspire to) human-like autonomy. Ultimately, understanding these evolutions and their interdependencies is pivotal for guiding future research, crafting responsible governance, and ensuring that AI’s transformative potential benefits society.