Towards real-time interest point detection and description for mobile and robotic devices
Convolutional Neural Networks (CNNs) have been successfully adopted by state-of-the-art feature point detection and description networks for the past number of years. The focus of these systems has been predominately on the accuracy of the system, rather than on its efficiency or ability to be imple...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2024-09-01
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| Series: | Informatics and Health |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2949953424000134 |
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| author | Patrick Rowsome Muhammad Adil Raja R. Muhammad Atif Azad |
| author_facet | Patrick Rowsome Muhammad Adil Raja R. Muhammad Atif Azad |
| author_sort | Patrick Rowsome |
| collection | DOAJ |
| description | Convolutional Neural Networks (CNNs) have been successfully adopted by state-of-the-art feature point detection and description networks for the past number of years. The focus of these systems has been predominately on the accuracy of the system, rather than on its efficiency or ability to be implemented in real-time on embedded robotic devices. This paper demonstrates how techniques, developed for other CNN use cases, can be integrated into interest point detection and description systems to compress their network size and reduce the computational complexity; this reduces the barrier to their uptake in computationally challenged environments. This paper documents the integration of these techniques into the popular Reliable Detector and Descriptor (R2D2) network. Along with the integration details, a comprehensive Key Performance Indicator (KPI) framework is developed to test all aspects of the networks. As a result, this paper presents a lightweight variant of the R2D2 network that significantly reduces parameters and computational complexity while crucially maintaining an acceptable level of accuracy. Consequently, this new compressed network is more appropriate for use in real world systems and advances the efforts to implement such CNN based system for mobile devices. |
| format | Article |
| id | doaj-art-8ae7ae59d03641d8a2be62c5f0901580 |
| institution | DOAJ |
| issn | 2949-9534 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Informatics and Health |
| spelling | doaj-art-8ae7ae59d03641d8a2be62c5f09015802025-08-20T03:21:19ZengKeAi Communications Co., Ltd.Informatics and Health2949-95342024-09-0112829210.1016/j.infoh.2024.06.002Towards real-time interest point detection and description for mobile and robotic devicesPatrick Rowsome0Muhammad Adil Raja1R. Muhammad Atif Azad2Protex AI, IrelandRegulated Software Research Center (RSRC), Dundalk Institute of Technology, Country Louth, Ireland; Corresponding author.School of Computing and Digital Technology, Birmingham City University, Birmingham, UKConvolutional Neural Networks (CNNs) have been successfully adopted by state-of-the-art feature point detection and description networks for the past number of years. The focus of these systems has been predominately on the accuracy of the system, rather than on its efficiency or ability to be implemented in real-time on embedded robotic devices. This paper demonstrates how techniques, developed for other CNN use cases, can be integrated into interest point detection and description systems to compress their network size and reduce the computational complexity; this reduces the barrier to their uptake in computationally challenged environments. This paper documents the integration of these techniques into the popular Reliable Detector and Descriptor (R2D2) network. Along with the integration details, a comprehensive Key Performance Indicator (KPI) framework is developed to test all aspects of the networks. As a result, this paper presents a lightweight variant of the R2D2 network that significantly reduces parameters and computational complexity while crucially maintaining an acceptable level of accuracy. Consequently, this new compressed network is more appropriate for use in real world systems and advances the efforts to implement such CNN based system for mobile devices.http://www.sciencedirect.com/science/article/pii/S2949953424000134Interest point detectionComputer visionConvolutional neural networksDeep learningReliable detector and descriptor |
| spellingShingle | Patrick Rowsome Muhammad Adil Raja R. Muhammad Atif Azad Towards real-time interest point detection and description for mobile and robotic devices Informatics and Health Interest point detection Computer vision Convolutional neural networks Deep learning Reliable detector and descriptor |
| title | Towards real-time interest point detection and description for mobile and robotic devices |
| title_full | Towards real-time interest point detection and description for mobile and robotic devices |
| title_fullStr | Towards real-time interest point detection and description for mobile and robotic devices |
| title_full_unstemmed | Towards real-time interest point detection and description for mobile and robotic devices |
| title_short | Towards real-time interest point detection and description for mobile and robotic devices |
| title_sort | towards real time interest point detection and description for mobile and robotic devices |
| topic | Interest point detection Computer vision Convolutional neural networks Deep learning Reliable detector and descriptor |
| url | http://www.sciencedirect.com/science/article/pii/S2949953424000134 |
| work_keys_str_mv | AT patrickrowsome towardsrealtimeinterestpointdetectionanddescriptionformobileandroboticdevices AT muhammadadilraja towardsrealtimeinterestpointdetectionanddescriptionformobileandroboticdevices AT rmuhammadatifazad towardsrealtimeinterestpointdetectionanddescriptionformobileandroboticdevices |