Existing artificial intelligence solutions typically operate in powerful platforms with high computational resources availability. However, a growing number of emerging use cases such as those based on unmanned aerial systems (UAS) require new solutions with embedded artificial intelligence on a highly mobile platform. This paper proposes an innovative UAS that explores machine learning (ML) capabilities in a smartphone‐based mobile platform for object detection and recognition applications. A new system framework tailored to this challenging use case is designed with a customized workflow specified. Furthermore, the design of the embedded ML leverages TensorFlow, a cutting‐edge open‐source ML framework. The prototype of the system integrates all the architectural components in a fully functional system, and it is suitable for real‐world operational environments such as seek and rescue use cases. Experimental results validate the design and prototyping of the system and demonstrate an overall improved performance compared with the state of the art in terms of a wide range of metrics.

中文翻译：

---

基于智能手机的带有嵌入式机器学习智能的无人机对象识别

现有的人工智能解决方案通常在具有高计算资源可用性的强大平台上运行。然而，越来越多的新兴用例，例如基于无人机系统 (UAS) 的用例，需要在高度移动的平台上嵌入人工智能的新解决方案。本文提出了一种创新的 UAS，它在基于智能手机的移动平台中探索机器学习 (ML) 功能，用于对象检测和识别应用。为这个具有挑战性的用例量身定制的新系统框架设计有指定的定制工作流。此外，嵌入式 ML 的设计利用了 TensorFlow，这是一种尖端的开源 ML 框架。系统原型将所有架构组件集成到一个功能齐全的系统中，它适用于现实世界的操作环境，例如搜索和救援用例。实验结果验证了系统的设计和原型设计，并在广泛的指标方面证明了与现有技术相比整体性能有所提高。