Cross-Platform Optimization of ROS 2-based Real-time Object Detection Systems 


Vol. 14,  No. 11, pp. 896-906, Nov.  2025
https://doi.org/10.3745/TKIPS.2025.14.11.896


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  Abstract

Real-time object detection is essential for ensuring the safety of autonomous vehicles. This study proposes three optimization techniques to improve the performance of ROS 2-based object detection services: GStreamer-based video input, client-server architecture improvement, and platform-optimized inference server implementation. On the Desktop platform, the client-server architecture improvement alone achieved over 55% performance enhancement compared to the baseline method. On the Jetson Orin Nano platform, the integrated application of all three techniques was required to achieve over 90% reduction in total processing time compared to the baseline. Furthermore, through analysis of CPU, memory, and network resources, we confirmed that the optimization techniques contribute not only to latency reduction but also to overall system efficiency improvement. This study demonstrates that optimization strategies vary depending o

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  Cite this article

[IEEE Style]

E. Hwang, S. Baik, Y. Hong, "Cross-Platform Optimization of ROS 2-based Real-time Object Detection Systems," The Transactions of the Korea Information Processing Society, vol. 14, no. 11, pp. 896-906, 2025. DOI: https://doi.org/10.3745/TKIPS.2025.14.11.896.

[ACM Style]

Eunjin Hwang, Seongbok Baik, and Yong-Geun Hong. 2025. Cross-Platform Optimization of ROS 2-based Real-time Object Detection Systems. The Transactions of the Korea Information Processing Society, 14, 11, (2025), 896-906. DOI: https://doi.org/10.3745/TKIPS.2025.14.11.896.