CBGS-YOLO: A Lightweight Network for Detecting Small Targets in Remote Sensing Images Based on a Double Attention Mechanism
With the continuous progress of remote sensing technology, the demand for means of detecting small targets in remote sensing images is escalating. The significance of detecting small targets in remote sensing images lies in enhancing the ability to identify small and elusive targets and the detectio...
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| Format: | Article |
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MDPI AG
2024-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/1/109 |
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| author | Zhenyuan Wu Di Wu Ning Li Wanru Chen Jie Yuan Xiangyue Yu Yongqiang Guo |
| author_facet | Zhenyuan Wu Di Wu Ning Li Wanru Chen Jie Yuan Xiangyue Yu Yongqiang Guo |
| author_sort | Zhenyuan Wu |
| collection | DOAJ |
| description | With the continuous progress of remote sensing technology, the demand for means of detecting small targets in remote sensing images is escalating. The significance of detecting small targets in remote sensing images lies in enhancing the ability to identify small and elusive targets and the detection accuracy against complex backgrounds, holding significant application value in military reconnaissance, environmental monitoring, and disaster early-warning systems. Firstly, the minuteness of certain targets in relation to the entire image in which they occur, particularly when the camera is situated at a higher altitude, renders them difficult to detect. Secondly, the varying background and lighting conditions in remote sensing images further complicate the detection task. Conventional target detection methods are frequently incapable of addressing these complexities, resulting in a reduction in detection accuracy and an increase in false alarms. Hence, in this paper, we propose a lightweight remote-sensing image target detection network model, CBGS-YOLO, created by introducing the Ghost module to decrease the model parameters, applying the SPD-Conv module to optimize downsampling, and integrating the convolutional block attention module to enhance detection accuracy. The experimental outcomes demonstrate that CBGS-YOLO outperforms other models when applied to the DB_Licenta and USOD datasets, significantly enhancing detection performance for small targets. Compared with YOLOv9, this model can reduce the number of parameters from 7.10 M to 5.12 M, and the average precision (mAP) is effectively improved. The model strengthens the ability to identify small targets against complex backgrounds while maintaining lightweight properties and possesses remarkable application prospects and practical value. |
| format | Article |
| id | doaj-art-c9099949bf964fc9bbaefbb0d3e8a72a |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-c9099949bf964fc9bbaefbb0d3e8a72a2025-01-10T13:20:15ZengMDPI AGRemote Sensing2072-42922024-12-0117110910.3390/rs17010109CBGS-YOLO: A Lightweight Network for Detecting Small Targets in Remote Sensing Images Based on a Double Attention MechanismZhenyuan Wu0Di Wu1Ning Li2Wanru Chen3Jie Yuan4Xiangyue Yu5Yongqiang Guo6Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaSchool of Civil Engineering and Environment, Hubei University of Technology, Wuhan 430068, ChinaSoftware College of North University of China, North University of China, Taiyuan 030051, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaWith the continuous progress of remote sensing technology, the demand for means of detecting small targets in remote sensing images is escalating. The significance of detecting small targets in remote sensing images lies in enhancing the ability to identify small and elusive targets and the detection accuracy against complex backgrounds, holding significant application value in military reconnaissance, environmental monitoring, and disaster early-warning systems. Firstly, the minuteness of certain targets in relation to the entire image in which they occur, particularly when the camera is situated at a higher altitude, renders them difficult to detect. Secondly, the varying background and lighting conditions in remote sensing images further complicate the detection task. Conventional target detection methods are frequently incapable of addressing these complexities, resulting in a reduction in detection accuracy and an increase in false alarms. Hence, in this paper, we propose a lightweight remote-sensing image target detection network model, CBGS-YOLO, created by introducing the Ghost module to decrease the model parameters, applying the SPD-Conv module to optimize downsampling, and integrating the convolutional block attention module to enhance detection accuracy. The experimental outcomes demonstrate that CBGS-YOLO outperforms other models when applied to the DB_Licenta and USOD datasets, significantly enhancing detection performance for small targets. Compared with YOLOv9, this model can reduce the number of parameters from 7.10 M to 5.12 M, and the average precision (mAP) is effectively improved. The model strengthens the ability to identify small targets against complex backgrounds while maintaining lightweight properties and possesses remarkable application prospects and practical value.https://www.mdpi.com/2072-4292/17/1/109dim target detectionlightweight networkremote sensing |
| spellingShingle | Zhenyuan Wu Di Wu Ning Li Wanru Chen Jie Yuan Xiangyue Yu Yongqiang Guo CBGS-YOLO: A Lightweight Network for Detecting Small Targets in Remote Sensing Images Based on a Double Attention Mechanism Remote Sensing dim target detection lightweight network remote sensing |
| title | CBGS-YOLO: A Lightweight Network for Detecting Small Targets in Remote Sensing Images Based on a Double Attention Mechanism |
| title_full | CBGS-YOLO: A Lightweight Network for Detecting Small Targets in Remote Sensing Images Based on a Double Attention Mechanism |
| title_fullStr | CBGS-YOLO: A Lightweight Network for Detecting Small Targets in Remote Sensing Images Based on a Double Attention Mechanism |
| title_full_unstemmed | CBGS-YOLO: A Lightweight Network for Detecting Small Targets in Remote Sensing Images Based on a Double Attention Mechanism |
| title_short | CBGS-YOLO: A Lightweight Network for Detecting Small Targets in Remote Sensing Images Based on a Double Attention Mechanism |
| title_sort | cbgs yolo a lightweight network for detecting small targets in remote sensing images based on a double attention mechanism |
| topic | dim target detection lightweight network remote sensing |
| url | https://www.mdpi.com/2072-4292/17/1/109 |
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