Evaluation on a Drone Classification Method Using UWB Radar Image Recognition with Deep Learning

Kawaguchi, Daiki; Nakamura, Ryohei; Hadama, Hisaya

doi:10.1109/vtc2021-spring51267.2021.9448946

Cited by 7 publications

(3 citation statements)

References 6 publications

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“…Furthermore, under the premise of meeting functionality requirements and protecting the environment, the design should then consider the aesthetics of culture and art, which will ensure that the guide system for each scenic area is one of a kind and distinctive. The display of regional cultural traits inside the scenic tour system has the potential to become a unique symbol that visitors can use to become familiar with the local culture and traditions [ 1 , 2 ].…”

Section: Introductionmentioning

confidence: 99%

Innovative Design of a Cloud Tour Guide Robot for Smart Cities under the Principle of System Innovation Design

Salim

Zakaria

et al. 2022

Computational Intelligence and Neuroscience

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In light of the ongoing occurrence of epidemics, the general populace frequently makes the decision to curtail their nomadic lifestyle in order to protect both their health and their safety. This has resulted in a number of issues, the most notable of which are the drop in the people’s living happiness index and the poor business that the tourism industry has been experiencing as a result. Therefore, the idea of “cloud tourism” is undoubtedly the first candidate for the tourism industry, and in order to meet the requirements of cloud tourism, it is necessary to have an entirely new system to serve this, of which the scenic guide robot is an important part. At the same time, the quickening development of 5G technology offers solutions that may be put into practice for the multifurther IoT’s expansion in smart cities. People will be able to experience the real outdoors without having to leave their homes, which will improve the people’s well-being and alleviate the chilly status quo in the tourism industry. This is the plan, and it will be accomplished through the use of innovative guide robots that will make the experience more convenient and reliable.

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Section: Introductionmentioning

confidence: 99%

Innovative Design of a Cloud Tour Guide Robot for Smart Cities under the Principle of System Innovation Design

Salim

Zakaria

et al. 2022

Computational Intelligence and Neuroscience

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“…More information on the number of drone datasets in this study is provided in [178]. [158] The datasets of following drones were divided in 3 categories: fixedwing (EasyStar_ETS, Microjet_LisaM), rotorcraft (ardrone2, Bumblebee_Quad, LadyLisa, Quad_LisaMX, Quad_NavGo, bebop, bebop2), versatile (bixler), and others (birds) with total number of samples around 27, 900 [159] Datasets contained 3 types of drones (Mavic pro, Phantom 3, Matrice 600) with a total of 6 drones. Training data included 50 frames of radar images (300 frames in total from all drones).…”

Section: Uav Classification Based On ML Using Radarmentioning

confidence: 99%

A Comprehensive Survey of Unmanned Aerial Vehicles Detection and Classification Using Machine Learning Approach: Challenges, Solutions, and Future Directions

Rahman,

Sejan,

Aziz

et al. 2024

Remote Sensing

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Autonomous unmanned aerial vehicles (UAVs) have several advantages in various fields, including disaster relief, aerial photography and videography, mapping and surveying, farming, as well as defense and public usage. However, there is a growing probability that UAVs could be misused to breach vital locations such as airports and power plants without authorization, endangering public safety. Because of this, it is critical to accurately and swiftly identify different types of UAVs to prevent their misuse and prevent security issues arising from unauthorized access. In recent years, machine learning (ML) algorithms have shown promise in automatically addressing the aforementioned concerns and providing accurate detection and classification of UAVs across a broad range. This technology is considered highly promising for UAV systems. In this survey, we describe the recent use of various UAV detection and classification technologies based on ML and deep learning (DL) algorithms. Four types of UAV detection and classification technologies based on ML are considered in this survey: radio frequency-based UAV detection, visual data (images/video)-based UAV detection, acoustic/sound-based UAV detection, and radar-based UAV detection. Additionally, this survey report explores hybrid sensor- and reinforcement learning-based UAV detection and classification using ML. Furthermore, we consider method challenges, solutions, and possible future research directions for ML-based UAV detection. Moreover, the dataset information of UAV detection and classification technologies is extensively explored. This investigation holds potential as a study for current UAV detection and classification research, particularly for ML- and DL-based UAV detection approaches.

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“…Haq et al [17] employ a stacked auto-encoder deep learning approach to achieve accurate forest area assessment using UAV-captured images applicable to forest management. Kawaguchi, Nakamura, and Hadama et al's research [18] leverages CNNs to identify diverse drone types, achieving over 90% accuracy in recognition and showcasing the prowess of CNNs in drone identification across various models and shapes, including radio-controlled flying objects [19].…”

Section: Introductionmentioning

confidence: 99%

Land-Cover Classification Using Deep Learning with High-Resolution Remote-Sensing Imagery

Fayaz,

Nam,

Dang

et al. 2024

Applied Sciences

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Land-area classification (LAC) research offers a promising avenue to address the intricacies of urban planning, agricultural zoning, and environmental monitoring, with a specific focus on urban areas and their complex land usage patterns. The potential of LAC research is significantly propelled by advancements in high-resolution satellite imagery and machine learning strategies, particularly the use of convolutional neural networks (CNNs). Accurate LAC is paramount for informed urban development and effective land management. Traditional remote-sensing methods encounter limitations in precisely classifying dynamic and complex urban land areas. Therefore, in this study, we investigated the application of transfer learning with Inception-v3 and DenseNet121 architectures to establish a reliable LAC system for identifying urban land use classes. Leveraging transfer learning with these models provided distinct advantages, as it allows the LAC system to benefit from pre-trained features on large datasets, enhancing model generalization and performance compared to starting from scratch. Transfer learning also facilitates the effective utilization of limited labeled data for fine-tuning, making it a valuable strategy for optimizing model accuracy in complex urban land classification tasks. Moreover, we strategically employ fine-tuned versions of Inception-v3 and DenseNet121 networks, emphasizing the transformative impact of these architectures. The fine-tuning process enables the model to leverage pre-existing knowledge from extensive datasets, enhancing its adaptability to the intricacies of LC classification. By aligning with these advanced techniques, our research not only contributes to the evolution of remote-sensing methodologies but also underscores the paramount importance of incorporating cutting-edge methodologies, such as fine-tuning and the use of specific network architectures, in the continual enhancement of LC classification systems. Through experiments conducted on the UC-Merced_LandUse dataset, we demonstrate the effectiveness of our approach, achieving remarkable results, including 92% accuracy, 93% recall, 92% precision, and a 92% F1-score. Moreover, employing heatmap analysis further elucidates the decision-making process of the models, providing insights into the classification mechanism. The successful application of CNNs in LAC, coupled with heatmap analysis, opens promising avenues for enhanced urban planning, agricultural zoning, and environmental monitoring through more accurate and automated land-area classification.

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Evaluation on a Drone Classification Method Using UWB Radar Image Recognition with Deep Learning

Cited by 7 publications

References 6 publications

Innovative Design of a Cloud Tour Guide Robot for Smart Cities under the Principle of System Innovation Design

Innovative Design of a Cloud Tour Guide Robot for Smart Cities under the Principle of System Innovation Design

A Comprehensive Survey of Unmanned Aerial Vehicles Detection and Classification Using Machine Learning Approach: Challenges, Solutions, and Future Directions

Land-Cover Classification Using Deep Learning with High-Resolution Remote-Sensing Imagery

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