Lightweighted Real-Time Object Detection on a Custom Edge Device 


Vol. 49,  No. 10, pp. 1447-1457, Oct.  2024
10.7840/kics.2024.49.10.1447


PDF
  Abstract

With recent innovations in AI and software technology, on-device object detection has drawn significant attention. This technique enables real-time processing of visual data without the need for a connection to a distant server. However, deploying these models on resource-constrained edge devices presents several challenges. The primary obstacles stem from the limited processing power, memory, and storage capacity of these devices, as well as software issues. The current constraints make training artificial intelligence inefficient, as it requires substantial storage and computational power. Moreover, the development of devices based on ARM architecture demands the training and implementation of a customized model specifically designed for that edge device. This article discusses the development of a lightweight object recognition model that utilizes a TensorFlow Lite model and achieves a high accuracy rate of 94% on a custom edge device. This study also presents techniques for implementing this method using a custom file, demonstrates new performance metrics, and yields favorable results compared to existing benchmarks.

  Statistics
Cumulative Counts from November, 2022
Multiple requests among the same browser session are counted as one view. If you mouse over a chart, the values of data points will be shown.


  Related Articles
  Cite this article

[IEEE Style]

M. J. A. Shanto, D. Kim, T. Jun, "Lightweighted Real-Time Object Detection on a Custom Edge Device," The Journal of Korean Institute of Communications and Information Sciences, vol. 49, no. 10, pp. 1447-1457, 2024. DOI: 10.7840/kics.2024.49.10.1447.

[ACM Style]

Md Javed Ahmed Shanto, Dong-Seong Kim, and Taesoo Jun. 2024. Lightweighted Real-Time Object Detection on a Custom Edge Device. The Journal of Korean Institute of Communications and Information Sciences, 49, 10, (2024), 1447-1457. DOI: 10.7840/kics.2024.49.10.1447.

[KICS Style]

Md Javed Ahmed Shanto, Dong-Seong Kim, Taesoo Jun, "Lightweighted Real-Time Object Detection on a Custom Edge Device," The Journal of Korean Institute of Communications and Information Sciences, vol. 49, no. 10, pp. 1447-1457, 10. 2024. (https://doi.org/10.7840/kics.2024.49.10.1447)
Vol. 49, No. 10 Index

Search
(IN TITLE, AUTHOR, ABSTRACT,KEYWORDS)

Advanced Search ▼

POPULAR KEYWORDS
(TOP 10 KEYWORDS)

Recent Publications
(LAST 3 YEARS)






pISSN : 1226 - 4717
eISSN : 2287 - 3880