Research Article
Open Access
Real-Time Object Detection Using a Lightweight Two-Stage Detection Network with Efficient Data Representation
1
Cardiff University, Cardiff CF10 3AT, United Kingdom
* Corresponding Author:
Shaohuang Wang,
[email protected]
Volume 1, Issue 1
2024
Received: 05 December 2023
Accepted: 16 April 2024
Published: 20 April 2024
Article Information
Abstract
In this paper, a novel fast object detection framework is introduced, designed to meet the needs of real-time applications such as autonomous driving and robot navigation. Traditional processing methods often trade off between accuracy and processing speed. To address this issue, a hybrid data representation method is proposed that combines the computational efficiency of voxelization with the detail capture capability of direct data processing to optimize overall performance. The detection framework comprises two main components: a Rapid Region Proposal Network (RPN) and a Refinement Detection Network (RefinerNet). The RPN is used to generate high-quality candidate regions, while the RefinerNet performs detailed analysis on these regions to improve detection accuracy. Additionally, a variety of network optimization strategies are incorporated, including lightweight depthwise separable convolutions and GPU-accelerated parallel inference, to increase processing speed and reduce computational resource consumption. Extensive testing on the KITTI and NEXET datasets has proven the effectiveness of this method in enhancing the accuracy of object detection and real-time processing speed. The experimental results show that, compared to existing technologies, this method performs exceptionally well across multiple evaluation metrics, especially in meeting the stringent requirements of real-time applications in terms of processing speed.
Graphical Abstract
Keywords
object detection
real-time
refinement
network optimization
Data Availability Statement
Data will be made available on request.
Funding
This work was supported without any funding.
Conflicts of Interest
The author declare no conflicts of interest.
Ethical Approval and Consent to Participate
Not applicable.
References
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APA Style
Wang, S.(2024). Real-Time Object Detection Using a Lightweight Two-Stage Detection Network with Efficient Data Representation. ICCK Transactions on Emerging Topics in Artificial Intelligence, 1(1), 17-30. https://doi.org/10.62762/TETAI.2024.320179
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TY - JOUR AU - Wang, Shaohuang PY - 2024 DA - 2024/04/20 TI - Real-Time Object Detection Using a Lightweight Two-Stage Detection Network with Efficient Data Representation JO - ICCK Transactions on Emerging Topics in Artificial Intelligence T2 - ICCK Transactions on Emerging Topics in Artificial Intelligence JF - ICCK Transactions on Emerging Topics in Artificial Intelligence VL - 1 IS - 1 SP - 17 EP - 30 DO - 10.62762/TETAI.2024.320179 UR - https://www.icck.org/article/abs/TETAI.2024.320179 KW - object detection KW - real-time KW - refinement KW - network optimization AB - In this paper, a novel fast object detection framework is introduced, designed to meet the needs of real-time applications such as autonomous driving and robot navigation. Traditional processing methods often trade off between accuracy and processing speed. To address this issue, a hybrid data representation method is proposed that combines the computational efficiency of voxelization with the detail capture capability of direct data processing to optimize overall performance. The detection framework comprises two main components: a Rapid Region Proposal Network (RPN) and a Refinement Detection Network (RefinerNet). The RPN is used to generate high-quality candidate regions, while the RefinerNet performs detailed analysis on these regions to improve detection accuracy. Additionally, a variety of network optimization strategies are incorporated, including lightweight depthwise separable convolutions and GPU-accelerated parallel inference, to increase processing speed and reduce computational resource consumption. Extensive testing on the KITTI and NEXET datasets has proven the effectiveness of this method in enhancing the accuracy of object detection and real-time processing speed. The experimental results show that, compared to existing technologies, this method performs exceptionally well across multiple evaluation metrics, especially in meeting the stringent requirements of real-time applications in terms of processing speed. SN - 3068-6652 PB - Institute of Central Computation and Knowledge LA - English ER -
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@article{Wang2024RealTime,
author = {Shaohuang Wang},
title = {Real-Time Object Detection Using a Lightweight Two-Stage Detection Network with Efficient Data Representation},
journal = {ICCK Transactions on Emerging Topics in Artificial Intelligence},
year = {2024},
volume = {1},
number = {1},
pages = {17-30},
doi = {10.62762/TETAI.2024.320179},
url = {https://www.icck.org/article/abs/TETAI.2024.320179},
abstract = {In this paper, a novel fast object detection framework is introduced, designed to meet the needs of real-time applications such as autonomous driving and robot navigation. Traditional processing methods often trade off between accuracy and processing speed. To address this issue, a hybrid data representation method is proposed that combines the computational efficiency of voxelization with the detail capture capability of direct data processing to optimize overall performance. The detection framework comprises two main components: a Rapid Region Proposal Network (RPN) and a Refinement Detection Network (RefinerNet). The RPN is used to generate high-quality candidate regions, while the RefinerNet performs detailed analysis on these regions to improve detection accuracy. Additionally, a variety of network optimization strategies are incorporated, including lightweight depthwise separable convolutions and GPU-accelerated parallel inference, to increase processing speed and reduce computational resource consumption. Extensive testing on the KITTI and NEXET datasets has proven the effectiveness of this method in enhancing the accuracy of object detection and real-time processing speed. The experimental results show that, compared to existing technologies, this method performs exceptionally well across multiple evaluation metrics, especially in meeting the stringent requirements of real-time applications in terms of processing speed.},
keywords = {object detection, real-time, refinement, network optimization},
issn = {3068-6652},
publisher = {Institute of Central Computation and Knowledge}
}
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Copyright © 2024 by the Author(s). Published by Institute of Central Computation and Knowledge. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
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