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首页> 外文期刊>Journal of Robotic Systems >Smartphone-based object recognition with embedded machine learning intelligence for unmanned aerial vehicles
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Smartphone-based object recognition with embedded machine learning intelligence for unmanned aerial vehicles

机译：基于智能手机的具有嵌入式机器学习智能的对象识别，适用于无人机

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摘要

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.
机译：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中运行。但是，越来越多的新兴用例，例如基于无人航空系统（UAS）的用例，需要在高度移动的平台上具有嵌入式人工智能的新解决方案。本文提出了一种创新的UAS，它可以在基于智能手机的移动平台中探索机器学习（ML）功能，以进行对象检测和识别应用。针对此具有挑战性的用例量身定制的新系统框架设计有指定的自定义工作流程。此外，嵌入式ML的设计利用了TensorFlow（一种先进的开源ML框架）。该系统的原型将所有架构组件集成到一个功能齐全的系统中，并且适用于诸如搜索和救援用例之类的实际操作环境。实验结果验证了系统的设计和原型制作，并在广泛的指标范围内证明了与现有技术相比总体上的改进。

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