Design Human Object Detection Yolov4-Tiny Algorithm on ARM Cortex-A72 and A53

Authors

  • Rachmat Muwardi Department of Electrical Engineering, Universitas Mercu Buana, Jakarta, Indonesia
  • Ahmad Faizin Department of Electrical Engineering, Universitas Mercu Buana, Jakarta, Indonesia
  • Puput Dani Prasetyo Adi Researcher at National Research and Innovation Agency (BRIN) http://orcid.org/0000-0002-5402-8864
  • Rizky Rahmatullah School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China
  • Yanxi Wang School of Electronics and Information Engineering, Beijing Institute of Technology, Beijing, China
  • Mirna Yunita School of Computer Science and Technology, Beijing Institute of Technology, China
  • Dendi Mahabror National Research and Innovation Agency, Indonesia

DOI:

https://doi.org/10.26555/jiteki.v9i4.27402

Keywords:

YOLOV4 Tiny, Optimization, Image Processing, Computer Vision, Object Detection, ARM Processor

Abstract

Currently, many object detection systems still use devices with large sizes, such as using PCs, as supporting devices, for object detection. This makes these devices challenging to use as a security system in public facilities based on human object detection. In contrast, many Mini PCs currently use ARM processors with high specifications. In this research, to detect human objects will use the Mini PC Nanopi M4V2 device that has a speed in processing with the support of CPU Dual-Core Cortex-A72 (up to 2.0 GHz) + Cortex A53 (Up to 2.0 GHz) and 4 Gb DDR4 Ram. In addition, for the human object detection system, the author uses the You Only Look Once (YOLO) method with the YoloV4-Tiny type, With these specifications and methods, the detection rate and FPS score are seen which are the feasibility values for use in detecting human objects. The simulation for human object recognition was carried out using recorded video, simulation obtained a detection rate of 0,9845 or 98% with FPS score of 3.81-5.55.  These results are the best when compared with the YOLOV4 and YOLOV5 models. With these results, it can be applied in various human detection applications and of course robustness testing is needed.

Author Biography

Puput Dani Prasetyo Adi, Researcher at National Research and Innovation Agency (BRIN)

Researcher at National Research and Innovation Agency (BRIN)

Downloads

Published

2024-01-05

How to Cite

[1]
R. Muwardi, “Design Human Object Detection Yolov4-Tiny Algorithm on ARM Cortex-A72 and A53”, J. Ilm. Tek. Elektro Komput. Dan Inform, vol. 9, no. 4, pp. 1168–1178, Jan. 2024.

Issue

Vol. 9 No. 4 (2023): December

Section

Articles

License

Authors who publish with JITEKI agree to the following terms:

  1. Authors retain copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.

Lisensi Creative Commons

This work is licensed under a Creative Commons Attribution 4.0 International License

Most read articles by the same author(s)