The Convergence of Deep Learning and Computer Vision: Smart City Applications and Research Challenges

Kothadiya, Deep; Chaudhari, Aayushi; Macwan, Ruchita; Patel, Krishna; Bhatt, Chintan

doi:10.2991/ahis.k.210913.003

Cited by 15 publications

(5 citation statements)

References 28 publications

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“…With a confidence score, each cell may anticipate a bounding box with a fixed B number. 5 values such as x, y, w, h, and confidence scores make up each bounding box [ 52 , 53 ]. The centres, width, and height of the bounding box are represented by respectively x, y, w, and h. After predicting a bounding box, IOU (intersection over union) is used by YOLO to find the correct bounding box of an object for the grid cell.…”

Section: Methodsmentioning

confidence: 99%

Deep Learning-Based Automatic Detection of Ships: An Experimental Study Using Satellite Images

2022

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The remote sensing surveillance of maritime areas represents an essential task for both security and environmental reasons. Recently, learning strategies belonging to the field of machine learning (ML) have become a niche of interest for the community of remote sensing. Specifically, a major challenge is the automatic classification of ships from satellite imagery, which is needed for traffic surveillance systems, the protection of illegal fisheries, control systems of oil discharge, and the monitoring of sea pollution. Deep learning (DL) is a branch of ML that has emerged in the last few years as a result of advancements in digital technology and data availability. DL has shown capacity and efficacy in tackling difficult learning tasks that were previously intractable. Specifically, DL methods, such as convolutional neural networks (CNNs), have been reported to be efficient in image detection and recognition applications. In this paper, we focused on the development of an automatic ship detection (ASD) approach by using DL methods for assessing the Airbus ship dataset (composed of about 40 K satellite images). The paper explores and analyzes the distinct variations of the YOLO algorithm for the detection of ships from satellite images. A comparison of different versions of YOLO algorithms for ship detection, such as YOLOv3, YOLOv4, and YOLOv5, is presented, after training them on a personal computer with a large dataset of satellite images of the Airbus Ship Challenge and Shipsnet. The differences between the algorithms could be observed on the personal computer. We have confirmed that these algorithms can be used for effective ship detection from satellite images. The conclusion drawn from the conducted research is that the YOLOv5 object detection algorithm outperforms the other versions of the YOLO algorithm, i.e., YOLOv4 and YOLOv3 in terms accuracy of 99% for YOLOv5 compared to 98% and 97% respectively for YOLOv4 and YOLOv3.

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Section: Methodsmentioning

confidence: 99%

Deep Learning-Based Automatic Detection of Ships: An Experimental Study Using Satellite Images

2022

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“…In AI, CV algorithms encompass a range of mathematical and computational techniques that empower machines to analyze and comprehend visual data obtained from their surroundings [127][128][129]. These algorithms can recognize patterns and features in images, videos, and other visual data and use that information to make decisions and predictions.…”

Section: Computer Vision (Cv)mentioning

confidence: 99%

Integration of IoT-Enabled Technologies and Artificial Intelligence (AI) for Smart City Scenario: Recent Advancements and Future Trends

Alahi

Sukkuea

Tina

et al. 2023

Sensors

140

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As the global population grows, and urbanization becomes more prevalent, cities often struggle to provide convenient, secure, and sustainable lifestyles due to the lack of necessary smart technologies. Fortunately, the Internet of Things (IoT) has emerged as a solution to this challenge by connecting physical objects using electronics, sensors, software, and communication networks. This has transformed smart city infrastructures, introducing various technologies that enhance sustainability, productivity, and comfort for urban dwellers. By leveraging Artificial Intelligence (AI) to analyze the vast amount of IoT data available, new opportunities are emerging to design and manage futuristic smart cities. In this review article, we provide an overview of smart cities, defining their characteristics and exploring the architecture of IoT. A detailed analysis of various wireless communication technologies employed in smart city applications is presented, with extensive research conducted to determine the most appropriate communication technologies for specific use cases. The article also sheds light on different AI algorithms and their suitability for smart city applications. Furthermore, the integration of IoT and AI in smart city scenarios is discussed, emphasizing the potential contributions of 5G networks coupled with AI in advancing modern urban environments. This article contributes to the existing literature by highlighting the tremendous opportunities presented by integrating IoT and AI, paving the way for the development of smart cities that significantly enhance the quality of life for urban dwellers while promoting sustainability and productivity. By exploring the potential of IoT, AI, and their integration, this review article provides valuable insights into the future of smart cities, demonstrating how these technologies can positively impact urban environments and the well-being of their inhabitants.

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“…Despite these potential benefits, smart city initiatives also come with several drawbacks. High implementation costs are one of the most significant challenges, as developing and implementing new technologies and infrastructure can be expensive and timeconsuming [22,49,50,52]. Increased privacy and security concerns are another potential disadvantage of smart city initiatives [6,36,54,79].…”

Section: Advantages and Disadvantagesmentioning

confidence: 99%

“…Technical challenges are among the most prominent factors that hinder the implementation of smart cities. Lack of standardization and interoperability are two primary technical challenges that cities face [2,8,16,22,23,27,44,49]. Smart city technologies are typically developed by different vendors and organizations, which can lead to fragmentation, complexity, and compatibility issues.…”

Section: Uncovering the Root Causes: Challenges In Implementing Smart...mentioning

confidence: 99%

“…Lastly, the category of Technology and Infrastructure is crucial for the success of smart city initiatives. Developing Open Data Platforms (ODPs) [5,14,45,53] and digital infrastructure that promote the sharing of data and enable the development of innovative solutions by various stakeholders, investing in existing infrastructure and developing new infrastructure, leveraging novel techniques such as machine learning algorithms [18,45], big data analytics [87], IoT, or cloud computing technologies, adopting an integrated, flexible, holistic, and adaptive approach to smart city planning, and using pilot projects to test and refine smart city solutions can all support the successful implementation of smart city initiatives [27,49,79].…”

Section: Strategies and Solutions Employed By Cities To Meet The Chal...mentioning

confidence: 99%

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Smart Cities—A Structured Literature Review

Gracias,

Parnell,

Specking

et al. 2023

Smart Cities

122

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Smart cities are rapidly evolving concept-transforming urban developments in the 21st century. Smart cities use advanced technologies and data analytics to improve the quality of life for their citizens, increase the efficiency of infrastructure and services, and promote sustainable economic growth. Smart cities integrate multiple domains, including transportation, energy, health, education, and governance, to create an interconnected and intelligent urban environment. Our research study methodology was a structured literature review using Web of Science and Google Scholar and ten smart city research questions. The research questions included smart city definitions, advantages, disadvantages, implementation challenges, funding, types of applications, quantitative techniques for analysis, and prioritization metrics. In addition, our study analyzes the implementation of smart city solutions in international contexts and proposes strategies to overcome implementation challenges. The integration of technology and data-driven solutions in smart cities has the potential to revolutionize urban living by providing citizens with personalized and accessible services. However, the implementation also presents challenges, including data privacy concerns, unequal access to technology, and the need for collaboration across private, public, and government sectors. This study provides insights into the current state and future prospects of smart cities and presents an analysis of the challenges and opportunities they present. In addition, we propose a concise definition for smart cities: “Smart cities use digital technologies, communication technologies, and data analytics to create an efficient and effective service environment that improves urban quality of life and promotes sustainability”. Smart cities represent a promising avenue for urban development. As cities continue to grow and face increasingly complex challenges, the integration of advanced technologies and data-driven solutions can help to create more sustainable communities.

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The Convergence of Deep Learning and Computer Vision: Smart City Applications and Research Challenges

Cited by 15 publications

References 28 publications

Deep Learning-Based Automatic Detection of Ships: An Experimental Study Using Satellite Images

Deep Learning-Based Automatic Detection of Ships: An Experimental Study Using Satellite Images

Integration of IoT-Enabled Technologies and Artificial Intelligence (AI) for Smart City Scenario: Recent Advancements and Future Trends

Smart Cities—A Structured Literature Review

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