IMPLEMENTATION OF A COMMUNICATION CHANNEL BETWEEN RASPBERRY PI AND ANDROID FOR REAL-TIME OBJECT DETECTION

Authors

  • Illia Bondarenko Petro Mohyla Black Sea National University
  • Kateryna Obukhova Petro Mohyla Black Sea National University

DOI:

https://doi.org/10.34132/mspc2025.01.06.08

Keywords:

robotic technical system, FPV drone, Raspberry Pi 4, TensorFlow Lite framework, object and obstacle detection.

Abstract

The paper are devoted to the integration of mobile and embedded platforms for the development of a hardware-software complex for objects and threats detection in real-time. Special attention is given to ensuring a stable and energy-efficient communication channel between system components.

References

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Support for Phantom 4. Retrieved from: https://www.dji.com/support/product/phantom-4 (Last accessed: 17.05.2025).

How to Run TensorFlow Lite Object Detection Models on the Raspberry Pi (with Optional Coral USB Accelerator). Retrieved from: https://github.com/EdjeElectronics/TensorFlow-Lite-Object-Detection-on-Android-and-Raspberry-Pi/blob/master/deploy_guides/Raspberry_Pi_Guide.md (Last accessed: 12.05.2025).

How to detect a single tree from a drone imagery of a dense forest? Nothing is simpler than custom object detection using TensorFlow. Retrieved from: https://blog.skygate.io/how-to-detect-a-single-tree-from-a-drone-imagery-of-a-dense-forest-de190f64cdb7 (Last accessed: 17.05.2025)

Published

2025-05-20