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Real-Time Object Detection Using a Lightweight Two-Stage Detection Network with Efficient Data Representation
Research Article | Published: 20 April 2024
IECE Transactions on Emerging Topics in Artificial Intelligence Volume 1, Issue 1, 2024: 17-30
Volume 1, Issue 1, IECE Transactions on Emerging Topics in Artificial Intelligence
Volume 1, Issue 1, 2024
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Teerath Kumar
Teerath Kumar
National College of Ireland, Ireland
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IECE Transactions on Emerging Topics in Artificial Intelligence, Volume 1, Issue 1, 2024: 17-30

Open Access | Research Article | 20 April 2024
Real-Time Object Detection Using a Lightweight Two-Stage Detection Network with Efficient Data Representation
by
Shaohuang Wang Shaohuang Wang 1 *
1 Cardiff University, Cardiff CF10 3AT, United Kingdom
* Corresponding Author: Shaohuang Wang, [email protected]
DOI: 10.62762/TETAI.2024.320179
Received: 05 December 2023, Accepted: 16 April 2024, Published: 20 April 2024  
Cited by: 5  (Source: Web of Science) , 5  (Source: Google Scholar)
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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 techniques have been implemented, including lightweight network layers, network pruning, and model quantization, 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
Real-Time Object Detection Using a Lightweight Two-Stage Detection Network with Efficient Data Representation
Keywords
object detection
real-time
refinement
network optimization
pruning
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.
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Cite This Article
APA Style
Wang, S.(2024). Real-Time Object Detection Using a Lightweight Two-Stage Detection Network with Efficient Data Representation. IECE Transactions on Emerging Topics in Artificial Intelligence, 1(1), 17–30. https://doi.org/10.62762/TETAI.2024.320179
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