Enhanced YOLOv8-based method for space debris detection using cross-scale feature fusion
Abstract Optical observations play a crucial role in monitoring space debris, and long exposure large field-of-view telescopes exhibit robust detection capabilities for identifying space debris. Nevertheless, a substantial volume of data, intricate noise, nonlinearity, and target discontinuities sig...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2025-01-01
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| Series: | Discover Applied Sciences |
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| Online Access: | https://doi.org/10.1007/s42452-025-06502-7 |
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| _version_ | 1832585478621102080 |
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| author | Yang Guo Xianlong Yin Yao Xiao Zhengxu Zhao Xu Yang Chenggang Dai |
| author_facet | Yang Guo Xianlong Yin Yao Xiao Zhengxu Zhao Xu Yang Chenggang Dai |
| author_sort | Yang Guo |
| collection | DOAJ |
| description | Abstract Optical observations play a crucial role in monitoring space debris, and long exposure large field-of-view telescopes exhibit robust detection capabilities for identifying space debris. Nevertheless, a substantial volume of data, intricate noise, nonlinearity, and target discontinuities significantly affect the observational process. To address these intricate celestial background conditions, an enhanced YOLOv8-based method for spatial debris detection is introduced into this study. Initially, a cross-scale feature fusion module is incorporated into the neck network, followed by a subtle processing step for the feature fusion component. Finally, the content-aware reassembly of features module is employed to replace the original upsampling module, which enhances the efficiency and accuracy of feature reconstruction, thereby achieving effective detection and identification of spatial debris targets. The study utilized a dataset comprising astronomical images captured by an open-source large-field-of-view optical telescope. The experimental results show that the detection accuracy and speed of the method are improved, and that they can meet the requirements of space debris detection in complex backgrounds. |
| format | Article |
| id | doaj-art-23999ac92ce549d3977462daf47ee0df |
| institution | Kabale University |
| issn | 3004-9261 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-23999ac92ce549d3977462daf47ee0df2025-01-26T12:47:30ZengSpringerDiscover Applied Sciences3004-92612025-01-017211410.1007/s42452-025-06502-7Enhanced YOLOv8-based method for space debris detection using cross-scale feature fusionYang Guo0Xianlong Yin1Yao Xiao2Zhengxu Zhao3Xu Yang4Chenggang Dai5Shandong Key Laboratory of Space Debris Monitoring and Low-Orbit Satellite Networking, Qingdao University of TechnologyShandong Key Laboratory of Space Debris Monitoring and Low-Orbit Satellite Networking, Qingdao University of TechnologyShandong Key Laboratory of Space Debris Monitoring and Low-Orbit Satellite Networking, Qingdao University of TechnologyShandong Key Laboratory of Space Debris Monitoring and Low-Orbit Satellite Networking, Qingdao University of TechnologyNational Astronomical Observatories, Chinese Academy of SciencesShandong Key Laboratory of Space Debris Monitoring and Low-Orbit Satellite Networking, Qingdao University of TechnologyAbstract Optical observations play a crucial role in monitoring space debris, and long exposure large field-of-view telescopes exhibit robust detection capabilities for identifying space debris. Nevertheless, a substantial volume of data, intricate noise, nonlinearity, and target discontinuities significantly affect the observational process. To address these intricate celestial background conditions, an enhanced YOLOv8-based method for spatial debris detection is introduced into this study. Initially, a cross-scale feature fusion module is incorporated into the neck network, followed by a subtle processing step for the feature fusion component. Finally, the content-aware reassembly of features module is employed to replace the original upsampling module, which enhances the efficiency and accuracy of feature reconstruction, thereby achieving effective detection and identification of spatial debris targets. The study utilized a dataset comprising astronomical images captured by an open-source large-field-of-view optical telescope. The experimental results show that the detection accuracy and speed of the method are improved, and that they can meet the requirements of space debris detection in complex backgrounds.https://doi.org/10.1007/s42452-025-06502-7Space debrisYOLOv8Target detectionFeature fusionFeature upsampling |
| spellingShingle | Yang Guo Xianlong Yin Yao Xiao Zhengxu Zhao Xu Yang Chenggang Dai Enhanced YOLOv8-based method for space debris detection using cross-scale feature fusion Discover Applied Sciences Space debris YOLOv8 Target detection Feature fusion Feature upsampling |
| title | Enhanced YOLOv8-based method for space debris detection using cross-scale feature fusion |
| title_full | Enhanced YOLOv8-based method for space debris detection using cross-scale feature fusion |
| title_fullStr | Enhanced YOLOv8-based method for space debris detection using cross-scale feature fusion |
| title_full_unstemmed | Enhanced YOLOv8-based method for space debris detection using cross-scale feature fusion |
| title_short | Enhanced YOLOv8-based method for space debris detection using cross-scale feature fusion |
| title_sort | enhanced yolov8 based method for space debris detection using cross scale feature fusion |
| topic | Space debris YOLOv8 Target detection Feature fusion Feature upsampling |
| url | https://doi.org/10.1007/s42452-025-06502-7 |
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