CM-YOLO: Typical Object Detection Method in Remote Sensing Cloud and Mist Scene Images
Remote sensing target detection technology in cloud and mist scenes is of great significance for applications such as marine safety monitoring and airport traffic management. However, the degradation and loss of features caused by the obstruction of cloud and mist elements still pose a challenging p...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/1/125 |
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| _version_ | 1841549001270231040 |
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| author | Jianming Hu Yangyu Wei Wenbin Chen Xiyang Zhi Wei Zhang |
| author_facet | Jianming Hu Yangyu Wei Wenbin Chen Xiyang Zhi Wei Zhang |
| author_sort | Jianming Hu |
| collection | DOAJ |
| description | Remote sensing target detection technology in cloud and mist scenes is of great significance for applications such as marine safety monitoring and airport traffic management. However, the degradation and loss of features caused by the obstruction of cloud and mist elements still pose a challenging problem for this technology. To enhance object detection performance in adverse weather conditions, we propose a novel target detection method named CM-YOLO that integrates background suppression and semantic context mining, which can achieve accurate detection of targets under different cloud and mist conditions. Specifically, a component-decoupling-based background suppression (CDBS) module is proposed, which extracts cloud and mist components based on characteristic priors and effectively enhances the contrast between the target and the environmental background through a background subtraction strategy. Moreover, a local-global semantic joint mining (LGSJM) module is utilized, which combines convolutional neural networks (CNNs) and hierarchical selective attention to comprehensively mine global and local semantics, achieving target feature enhancement. Finally, the experimental results on multiple public datasets indicate that the proposed method realizes state-of-the-art performance compared to six advanced detectors, with mAP, precision, and recall indicators reaching 85.5%, 89.4%, and 77.9%, respectively. |
| format | Article |
| id | doaj-art-7f495ffb51694fd88a170f5a476bfdd7 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-7f495ffb51694fd88a170f5a476bfdd72025-01-10T13:20:18ZengMDPI AGRemote Sensing2072-42922025-01-0117112510.3390/rs17010125CM-YOLO: Typical Object Detection Method in Remote Sensing Cloud and Mist Scene ImagesJianming Hu0Yangyu Wei1Wenbin Chen2Xiyang Zhi3Wei Zhang4Research Center for Space Optical Engineering, Harbin Institute of Technology, Harbin 150001, ChinaResearch Center for Space Optical Engineering, Harbin Institute of Technology, Harbin 150001, ChinaResearch Center for Space Optical Engineering, Harbin Institute of Technology, Harbin 150001, ChinaResearch Center for Space Optical Engineering, Harbin Institute of Technology, Harbin 150001, ChinaResearch Center for Space Optical Engineering, Harbin Institute of Technology, Harbin 150001, ChinaRemote sensing target detection technology in cloud and mist scenes is of great significance for applications such as marine safety monitoring and airport traffic management. However, the degradation and loss of features caused by the obstruction of cloud and mist elements still pose a challenging problem for this technology. To enhance object detection performance in adverse weather conditions, we propose a novel target detection method named CM-YOLO that integrates background suppression and semantic context mining, which can achieve accurate detection of targets under different cloud and mist conditions. Specifically, a component-decoupling-based background suppression (CDBS) module is proposed, which extracts cloud and mist components based on characteristic priors and effectively enhances the contrast between the target and the environmental background through a background subtraction strategy. Moreover, a local-global semantic joint mining (LGSJM) module is utilized, which combines convolutional neural networks (CNNs) and hierarchical selective attention to comprehensively mine global and local semantics, achieving target feature enhancement. Finally, the experimental results on multiple public datasets indicate that the proposed method realizes state-of-the-art performance compared to six advanced detectors, with mAP, precision, and recall indicators reaching 85.5%, 89.4%, and 77.9%, respectively.https://www.mdpi.com/2072-4292/17/1/125aircraft and ship detectioncloud and mist interferencesbackground suppressionsemantic joint miningoptical image |
| spellingShingle | Jianming Hu Yangyu Wei Wenbin Chen Xiyang Zhi Wei Zhang CM-YOLO: Typical Object Detection Method in Remote Sensing Cloud and Mist Scene Images Remote Sensing aircraft and ship detection cloud and mist interferences background suppression semantic joint mining optical image |
| title | CM-YOLO: Typical Object Detection Method in Remote Sensing Cloud and Mist Scene Images |
| title_full | CM-YOLO: Typical Object Detection Method in Remote Sensing Cloud and Mist Scene Images |
| title_fullStr | CM-YOLO: Typical Object Detection Method in Remote Sensing Cloud and Mist Scene Images |
| title_full_unstemmed | CM-YOLO: Typical Object Detection Method in Remote Sensing Cloud and Mist Scene Images |
| title_short | CM-YOLO: Typical Object Detection Method in Remote Sensing Cloud and Mist Scene Images |
| title_sort | cm yolo typical object detection method in remote sensing cloud and mist scene images |
| topic | aircraft and ship detection cloud and mist interferences background suppression semantic joint mining optical image |
| url | https://www.mdpi.com/2072-4292/17/1/125 |
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