使用基于深度学习的计算机视觉框架进行无人机自主导航:系统性文献综述

IF 2.3 Q2 COMPUTER SCIENCE, THEORY & METHODS
Array Pub Date : 2024-09-01 DOI:10.1016/j.array.2024.100361
Aditya Vardhan Reddy Katkuri , Hakka Madan , Narendra Khatri , Antar Shaddad Hamed Abdul-Qawy , K. Sridhar Patnaik
{"title":"使用基于深度学习的计算机视觉框架进行无人机自主导航:系统性文献综述","authors":"Aditya Vardhan Reddy Katkuri ,&nbsp;Hakka Madan ,&nbsp;Narendra Khatri ,&nbsp;Antar Shaddad Hamed Abdul-Qawy ,&nbsp;K. Sridhar Patnaik","doi":"10.1016/j.array.2024.100361","DOIUrl":null,"url":null,"abstract":"<div><p>The increasing use of unmanned aerial vehicles (UAVs) in both military and civilian applications, such as infrastructure inspection, package delivery, and recreational activities, underscores the importance of enhancing their autonomous functionalities. Artificial intelligence (AI), particularly deep learning-based computer vision (DL-based CV), plays a crucial role in this enhancement. This paper aims to provide a systematic literature review (SLR) of Scopus-indexed research studies published from 2019 to 2024, focusing on DL-based CV approaches for autonomous UAV applications. By analyzing 173 studies, we categorize the research into four domains: sensing and inspection, landing, surveillance and tracking, and search and rescue. Our review reveals a significant increase in research utilizing computer vision for UAV applications, with over 39.5 % of studies employing the You Only Look Once (YOLO) framework. We discuss the key findings, including the dominant trends, challenges, and opportunities in the field, and highlight emerging technologies such as in-sensor computing. This review provides valuable insights into the current state and future directions of DL-based CV for autonomous UAVs, emphasizing its growing significance as legislative frameworks evolve to support these technologies.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"23 ","pages":"Article 100361"},"PeriodicalIF":2.3000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590005624000274/pdfft?md5=49538d0ae336567b2c721a5cb431f7e9&pid=1-s2.0-S2590005624000274-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Autonomous UAV navigation using deep learning-based computer vision frameworks: A systematic literature review\",\"authors\":\"Aditya Vardhan Reddy Katkuri ,&nbsp;Hakka Madan ,&nbsp;Narendra Khatri ,&nbsp;Antar Shaddad Hamed Abdul-Qawy ,&nbsp;K. Sridhar Patnaik\",\"doi\":\"10.1016/j.array.2024.100361\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The increasing use of unmanned aerial vehicles (UAVs) in both military and civilian applications, such as infrastructure inspection, package delivery, and recreational activities, underscores the importance of enhancing their autonomous functionalities. Artificial intelligence (AI), particularly deep learning-based computer vision (DL-based CV), plays a crucial role in this enhancement. This paper aims to provide a systematic literature review (SLR) of Scopus-indexed research studies published from 2019 to 2024, focusing on DL-based CV approaches for autonomous UAV applications. By analyzing 173 studies, we categorize the research into four domains: sensing and inspection, landing, surveillance and tracking, and search and rescue. Our review reveals a significant increase in research utilizing computer vision for UAV applications, with over 39.5 % of studies employing the You Only Look Once (YOLO) framework. We discuss the key findings, including the dominant trends, challenges, and opportunities in the field, and highlight emerging technologies such as in-sensor computing. This review provides valuable insights into the current state and future directions of DL-based CV for autonomous UAVs, emphasizing its growing significance as legislative frameworks evolve to support these technologies.</p></div>\",\"PeriodicalId\":8417,\"journal\":{\"name\":\"Array\",\"volume\":\"23 \",\"pages\":\"Article 100361\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2024-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2590005624000274/pdfft?md5=49538d0ae336567b2c721a5cb431f7e9&pid=1-s2.0-S2590005624000274-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Array\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2590005624000274\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, THEORY & METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Array","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2590005624000274","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0

摘要

无人驾驶飞行器(UAV)在基础设施检测、包裹递送和娱乐活动等军事和民用领域的应用日益增多,这凸显了增强其自主功能的重要性。人工智能(AI),尤其是基于深度学习的计算机视觉(DL-based CV),在这种增强中发挥着至关重要的作用。本文旨在对 2019 年至 2024 年期间发表的 Scopus 索引研究进行系统性文献综述(SLR),重点关注自主无人机应用中基于 DL 的 CV 方法。通过分析 173 项研究,我们将研究分为四个领域:感知和检测、着陆、监视和跟踪以及搜索和救援。我们的综述显示,利用计算机视觉进行无人机应用的研究大幅增加,超过 39.5% 的研究采用了 "只看一遍"(YOLO)框架。我们讨论了主要发现,包括该领域的主要趋势、挑战和机遇,并重点介绍了传感器内计算等新兴技术。本综述为自主无人机基于 DL 的 CV 的现状和未来发展方向提供了有价值的见解,并强调了随着支持这些技术的立法框架不断发展,其重要性也在不断增加。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Autonomous UAV navigation using deep learning-based computer vision frameworks: A systematic literature review

The increasing use of unmanned aerial vehicles (UAVs) in both military and civilian applications, such as infrastructure inspection, package delivery, and recreational activities, underscores the importance of enhancing their autonomous functionalities. Artificial intelligence (AI), particularly deep learning-based computer vision (DL-based CV), plays a crucial role in this enhancement. This paper aims to provide a systematic literature review (SLR) of Scopus-indexed research studies published from 2019 to 2024, focusing on DL-based CV approaches for autonomous UAV applications. By analyzing 173 studies, we categorize the research into four domains: sensing and inspection, landing, surveillance and tracking, and search and rescue. Our review reveals a significant increase in research utilizing computer vision for UAV applications, with over 39.5 % of studies employing the You Only Look Once (YOLO) framework. We discuss the key findings, including the dominant trends, challenges, and opportunities in the field, and highlight emerging technologies such as in-sensor computing. This review provides valuable insights into the current state and future directions of DL-based CV for autonomous UAVs, emphasizing its growing significance as legislative frameworks evolve to support these technologies.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Array
Array Computer Science-General Computer Science
CiteScore
4.40
自引率
0.00%
发文量
93
审稿时长
45 days
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信