Status and challenges of UAV recognition methods based on deep learning
The wide range of military，civil，and commercial applications of UAVs has prompted the need for their recognition and classification. With the development of artificial intelligence，deep learning，as a machine learning technique，has shown good performance in the field of object detection，and is also a...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Advances in Aeronautical Science and Engineering
2025-04-01
|
| Series: | Hangkong gongcheng jinzhan |
| Subjects: | |
| Online Access: | http://hkgcjz.cnjournals.com/hkgcjz/article/abstract/2023244 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850171289502744576 |
|---|---|
| author | LI Qiqin YANG Ming REN Haotian CHANG Haoliang ZHANG Xiaoqiang ZHU Xinyu |
| author_facet | LI Qiqin YANG Ming REN Haotian CHANG Haoliang ZHANG Xiaoqiang ZHU Xinyu |
| author_sort | LI Qiqin |
| collection | DOAJ |
| description | The wide range of military，civil，and commercial applications of UAVs has prompted the need for their recognition and classification. With the development of artificial intelligence，deep learning，as a machine learning technique，has shown good performance in the field of object detection，and is also applied to the field of UAV recognition. This paper firstly introduces the background and significance of UAV recognition，reviews the development history of deep learning，and introduces two important algorithm structures in object detection：two-stage detector and single-stage detector. Secondly，it describes the common algorithms for object detection and the backbone network in the algorithms，and then summarises the improvement strategies of improved algorithms for UAV recognition in recent years，and summarises the improvement effect and its shortcomings and limitations. Finally， the outlook and challenges are discussed with respect to the current research status of UAV recognition，which is expected to make greater breakthroughs in establishing UAV datasets，improving the accuracy and real-time performance of UAV detection，and promoting the application of UAV technology in various fields. |
| format | Article |
| id | doaj-art-ffe2021706f141ac9c8aaecefc55a419 |
| institution | OA Journals |
| issn | 1674-8190 |
| language | zho |
| publishDate | 2025-04-01 |
| publisher | Editorial Department of Advances in Aeronautical Science and Engineering |
| record_format | Article |
| series | Hangkong gongcheng jinzhan |
| spelling | doaj-art-ffe2021706f141ac9c8aaecefc55a4192025-08-20T02:20:18ZzhoEditorial Department of Advances in Aeronautical Science and EngineeringHangkong gongcheng jinzhan1674-81902025-04-0116211110.16615/j.cnki.1674-8190.2025.02.0120250201Status and challenges of UAV recognition methods based on deep learningLI Qiqin0YANG Ming1REN Haotian2CHANG Haoliang3ZHANG Xiaoqiang4ZHU Xinyu5Institute of Electronic and Electrical Engineering, Civil Aviation Flight University of China, Guanghan 618307, ChinaInstitute of Electronic and Electrical Engineering, Civil Aviation Flight University of China, Guanghan 618307, ChinaInstitute of Electronic and Electrical Engineering, Civil Aviation Flight University of China, Guanghan 618307, ChinaInstitute of Electronic and Electrical Engineering, Civil Aviation Flight University of China, Guanghan 618307, ChinaInstitute of Electronic and Electrical Engineering, Civil Aviation Flight University of China, Guanghan 618307, ChinaInstitute of Electronic and Electrical Engineering, Civil Aviation Flight University of China, Guanghan 618307, ChinaThe wide range of military，civil，and commercial applications of UAVs has prompted the need for their recognition and classification. With the development of artificial intelligence，deep learning，as a machine learning technique，has shown good performance in the field of object detection，and is also applied to the field of UAV recognition. This paper firstly introduces the background and significance of UAV recognition，reviews the development history of deep learning，and introduces two important algorithm structures in object detection：two-stage detector and single-stage detector. Secondly，it describes the common algorithms for object detection and the backbone network in the algorithms，and then summarises the improvement strategies of improved algorithms for UAV recognition in recent years，and summarises the improvement effect and its shortcomings and limitations. Finally， the outlook and challenges are discussed with respect to the current research status of UAV recognition，which is expected to make greater breakthroughs in establishing UAV datasets，improving the accuracy and real-time performance of UAV detection，and promoting the application of UAV technology in various fields.http://hkgcjz.cnjournals.com/hkgcjz/article/abstract/2023244object detectionunmanned aerial vehicledeep learningcomputer visionneural networks |
| spellingShingle | LI Qiqin YANG Ming REN Haotian CHANG Haoliang ZHANG Xiaoqiang ZHU Xinyu Status and challenges of UAV recognition methods based on deep learning Hangkong gongcheng jinzhan object detection unmanned aerial vehicle deep learning computer vision neural networks |
| title | Status and challenges of UAV recognition methods based on deep learning |
| title_full | Status and challenges of UAV recognition methods based on deep learning |
| title_fullStr | Status and challenges of UAV recognition methods based on deep learning |
| title_full_unstemmed | Status and challenges of UAV recognition methods based on deep learning |
| title_short | Status and challenges of UAV recognition methods based on deep learning |
| title_sort | status and challenges of uav recognition methods based on deep learning |
| topic | object detection unmanned aerial vehicle deep learning computer vision neural networks |
| url | http://hkgcjz.cnjournals.com/hkgcjz/article/abstract/2023244 |
| work_keys_str_mv | AT liqiqin statusandchallengesofuavrecognitionmethodsbasedondeeplearning AT yangming statusandchallengesofuavrecognitionmethodsbasedondeeplearning AT renhaotian statusandchallengesofuavrecognitionmethodsbasedondeeplearning AT changhaoliang statusandchallengesofuavrecognitionmethodsbasedondeeplearning AT zhangxiaoqiang statusandchallengesofuavrecognitionmethodsbasedondeeplearning AT zhuxinyu statusandchallengesofuavrecognitionmethodsbasedondeeplearning |