Swin‐YOLOX for autonomous and accurate drone visual landing
Abstract As UAVs are more and more widely used in military and civilian fields, their intelligent applications have also been developed rapidly. However, high‐precision autonomous landing is still an industry challenge. GPS‐based methods will not work in places where GPS signals are not available; m...
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | IET Image Processing |
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| Online Access: | https://doi.org/10.1049/ipr2.13282 |
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| author | Rongbin Chen Ying Xu Mohamad Sabri bin Sinal Dongsheng Zhong Xinru Li Bo Li Yadong Guo Qingjia Luo |
| author_facet | Rongbin Chen Ying Xu Mohamad Sabri bin Sinal Dongsheng Zhong Xinru Li Bo Li Yadong Guo Qingjia Luo |
| author_sort | Rongbin Chen |
| collection | DOAJ |
| description | Abstract As UAVs are more and more widely used in military and civilian fields, their intelligent applications have also been developed rapidly. However, high‐precision autonomous landing is still an industry challenge. GPS‐based methods will not work in places where GPS signals are not available; multi‐sensor combination navigation is difficult to be widely used because of the high equipment requirements; traditional vision‐based methods are sensitive to scale transformation, background complexity and occlusion, which affect the detection performance. In this paper, we address these problems and apply deep learning methods to target detection in the UAV landing phase. Firstly, we optimize the backbone network of YOLOX and propose the Swin Transformer based YOLOX (Swin‐YOLOX) UAV landing visual positioning algorithm. Secondly, based on the UAV‐VPD database, a batch of actual acquisition data is added to build the UAV‐VPDV2 database by AI annotation method. And finally, the RBN data batch normalization method is used to improve the performance of the model in extracting effective features from the data. Extensive experiments have shown that the AP50 of the proposed method can reach 98.7%, which is superior to other detection models, with a detection speed of 38.4 frames/second, and can meet the requirements of real‐time detection. |
| format | Article |
| id | doaj-art-9732be50acfa4e489a970099160d05c3 |
| institution | OA Journals |
| issn | 1751-9659 1751-9667 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Image Processing |
| spelling | doaj-art-9732be50acfa4e489a970099160d05c32025-08-20T02:35:53ZengWileyIET Image Processing1751-96591751-96672024-12-0118144731474410.1049/ipr2.13282Swin‐YOLOX for autonomous and accurate drone visual landingRongbin Chen0Ying Xu1Mohamad Sabri bin Sinal2Dongsheng Zhong3Xinru Li4Bo Li5Yadong Guo6Qingjia Luo7College of Information Engineering, Jiangmen Polytechnic Jiangmen Guangdong ChinaDepartment of Intelligent Manufacturing Wuyi University Jiangmen ChinaSchool of Computing Universiti Utara Malaysia Kedah MalaysiaDepartment of Intelligent Manufacturing Wuyi University Jiangmen ChinaDepartment of Intelligent Manufacturing Wuyi University Jiangmen ChinaDepartment of Intelligent Manufacturing Wuyi University Jiangmen ChinaCollege of Information Engineering, Jiangmen Polytechnic Jiangmen Guangdong ChinaCollege of Information Engineering, Jiangmen Polytechnic Jiangmen Guangdong ChinaAbstract As UAVs are more and more widely used in military and civilian fields, their intelligent applications have also been developed rapidly. However, high‐precision autonomous landing is still an industry challenge. GPS‐based methods will not work in places where GPS signals are not available; multi‐sensor combination navigation is difficult to be widely used because of the high equipment requirements; traditional vision‐based methods are sensitive to scale transformation, background complexity and occlusion, which affect the detection performance. In this paper, we address these problems and apply deep learning methods to target detection in the UAV landing phase. Firstly, we optimize the backbone network of YOLOX and propose the Swin Transformer based YOLOX (Swin‐YOLOX) UAV landing visual positioning algorithm. Secondly, based on the UAV‐VPD database, a batch of actual acquisition data is added to build the UAV‐VPDV2 database by AI annotation method. And finally, the RBN data batch normalization method is used to improve the performance of the model in extracting effective features from the data. Extensive experiments have shown that the AP50 of the proposed method can reach 98.7%, which is superior to other detection models, with a detection speed of 38.4 frames/second, and can meet the requirements of real‐time detection.https://doi.org/10.1049/ipr2.13282computer visionimage processingimage recognitionremote sensing |
| spellingShingle | Rongbin Chen Ying Xu Mohamad Sabri bin Sinal Dongsheng Zhong Xinru Li Bo Li Yadong Guo Qingjia Luo Swin‐YOLOX for autonomous and accurate drone visual landing IET Image Processing computer vision image processing image recognition remote sensing |
| title | Swin‐YOLOX for autonomous and accurate drone visual landing |
| title_full | Swin‐YOLOX for autonomous and accurate drone visual landing |
| title_fullStr | Swin‐YOLOX for autonomous and accurate drone visual landing |
| title_full_unstemmed | Swin‐YOLOX for autonomous and accurate drone visual landing |
| title_short | Swin‐YOLOX for autonomous and accurate drone visual landing |
| title_sort | swin yolox for autonomous and accurate drone visual landing |
| topic | computer vision image processing image recognition remote sensing |
| url | https://doi.org/10.1049/ipr2.13282 |
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