Engineering Challenges for AI-Supported Computer Vision in Small Uncrewed Aerial Systems
Engineering Challenges for AI-Supported Computer Vision in Small Uncrewed Aerial Systems
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DOI：
10.1109/cain58948.2023.00033
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发表时间：
2023-05
期刊：
影响因子：
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通讯作者：
Muhammed Tawfiq Chowdhury;J. Cleland-Huang
中科院分区：
文献类型：
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作者：
Muhammed Tawfiq Chowdhury;J. Cleland-Huang
计算机视觉（CV）用于广泛的网络物理系统，例如手术和工厂车间机器人以及包括小型无人机系统（sUAS）在内的自动驾驶车辆。它使机器能够通过检测和分类感兴趣的对象、重建3D场景、估计运动和在对象周围机动来感知世界。CV算法是使用各种机器学习和深度学习框架开发的，这些框架通常部署在资源有限的边缘设备上。由于sUAS依赖于对其环境的准确和及时的感知来执行关键任务，因此与CV相关的问题可能会产生导致坠毁或使命失败的危险条件。在本文中，我们对CV、硬件和软件工程相关的CV相关挑战进行了系统的文献综述（SLR）。然后，我们将报告的挑战分为五个类别和十四个子挑战，并提出现有的解决方案。由于目前的文献主要集中在CV和硬件的挑战，我们关闭讨论软件工程的影响，从CV增强的多sUAS系统的例子。
Computer Vision (CV) is used in a broad range of Cyber-Physical Systems such as surgical and factory floor robots and autonomous vehicles including small Unmanned Aerial Systems (sUAS). It enables machines to perceive the world by detecting and classifying objects of interest, reconstructing 3D scenes, estimating motion, and maneuvering around objects. CV algorithms are developed using diverse machine learning and deep learning frameworks, which are often deployed on limited resource edge devices. As sUAS rely upon an accurate and timely perception of their environment to perform critical tasks, problems related to CV can create hazardous conditions leading to crashes or mission failure. In this paper, we perform a systematic literature review (SLR) of CV-related challenges associated with CV, hardware, and software engineering. We then group the reported challenges into five categories and fourteen sub-challenges and present existing solutions. As current literature focuses primarily on CV and hardware challenges, we close by discussing implications for Software Engineering, drawing examples from a CV-enhanced multi-sUAS system.