SCM-YOLO for Lightweight Small Object Detection in Remote Sensing Images
Currently, small object detection in complex remote sensing environments faces significant challenges. The detectors designed for this scenario have limitations, such as insufficient extraction of spatial local information, inflexible feature fusion, and limited global feature acquisition capability...
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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/2072-4292/17/2/249 |
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| author | Hao Qiang Wei Hao Meilin Xie Qiang Tang Heng Shi Yixin Zhao Xiaoteng Han |
| author_facet | Hao Qiang Wei Hao Meilin Xie Qiang Tang Heng Shi Yixin Zhao Xiaoteng Han |
| author_sort | Hao Qiang |
| collection | DOAJ |
| description | Currently, small object detection in complex remote sensing environments faces significant challenges. The detectors designed for this scenario have limitations, such as insufficient extraction of spatial local information, inflexible feature fusion, and limited global feature acquisition capability. In addition, there is a need to balance performance and complexity when improving the model. To address these issues, this paper proposes an efficient and lightweight SCM-YOLO detector improved from YOLOv5 with spatial local information enhancement, multi-scale feature adaptive fusion, and global sensing capabilities. The SCM-YOLO detector consists of three innovative and lightweight modules: the Space Interleaving in Depth (SPID) module, the Cross Block and Channel Reweight Concat (CBCC) module, and the Mixed Local Channel Attention Global Integration (MAGI) module. These three modules effectively improve the performance of the detector from three aspects: feature extraction, feature fusion, and feature perception. The ability of SCM-YOLO to detect small objects in complex remote sensing environments has been significantly improved while maintaining its lightweight characteristics. The effectiveness and lightweight characteristics of SCM-YOLO are verified through comparison experiments with AI-TOD and SIMD public remote sensing small object detection datasets. In addition, we validate the effectiveness of the three modules, SPID, CBCC, and MAGI, through ablation experiments. The comparison experiments on the AI-TOD dataset show that the mAP50 and mAP50-95 metrics of SCM-YOLO reach 64.053% and 27.283%, respectively, which are significantly better than other models with the same parameter size. |
| format | Article |
| id | doaj-art-80c970a1ac65458d9cf407e1a566b881 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-80c970a1ac65458d9cf407e1a566b8812025-01-24T13:47:52ZengMDPI AGRemote Sensing2072-42922025-01-0117224910.3390/rs17020249SCM-YOLO for Lightweight Small Object Detection in Remote Sensing ImagesHao Qiang0Wei Hao1Meilin Xie2Qiang Tang3Heng Shi4Yixin Zhao5Xiaoteng Han6Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, ChinaXi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, ChinaXi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, ChinaXi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, ChinaXi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, ChinaXi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, ChinaXi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, ChinaCurrently, small object detection in complex remote sensing environments faces significant challenges. The detectors designed for this scenario have limitations, such as insufficient extraction of spatial local information, inflexible feature fusion, and limited global feature acquisition capability. In addition, there is a need to balance performance and complexity when improving the model. To address these issues, this paper proposes an efficient and lightweight SCM-YOLO detector improved from YOLOv5 with spatial local information enhancement, multi-scale feature adaptive fusion, and global sensing capabilities. The SCM-YOLO detector consists of three innovative and lightweight modules: the Space Interleaving in Depth (SPID) module, the Cross Block and Channel Reweight Concat (CBCC) module, and the Mixed Local Channel Attention Global Integration (MAGI) module. These three modules effectively improve the performance of the detector from three aspects: feature extraction, feature fusion, and feature perception. The ability of SCM-YOLO to detect small objects in complex remote sensing environments has been significantly improved while maintaining its lightweight characteristics. The effectiveness and lightweight characteristics of SCM-YOLO are verified through comparison experiments with AI-TOD and SIMD public remote sensing small object detection datasets. In addition, we validate the effectiveness of the three modules, SPID, CBCC, and MAGI, through ablation experiments. The comparison experiments on the AI-TOD dataset show that the mAP50 and mAP50-95 metrics of SCM-YOLO reach 64.053% and 27.283%, respectively, which are significantly better than other models with the same parameter size.https://www.mdpi.com/2072-4292/17/2/249small object detectionremote sensing imagespatial local informationfeature fusionattention mechanism |
| spellingShingle | Hao Qiang Wei Hao Meilin Xie Qiang Tang Heng Shi Yixin Zhao Xiaoteng Han SCM-YOLO for Lightweight Small Object Detection in Remote Sensing Images Remote Sensing small object detection remote sensing image spatial local information feature fusion attention mechanism |
| title | SCM-YOLO for Lightweight Small Object Detection in Remote Sensing Images |
| title_full | SCM-YOLO for Lightweight Small Object Detection in Remote Sensing Images |
| title_fullStr | SCM-YOLO for Lightweight Small Object Detection in Remote Sensing Images |
| title_full_unstemmed | SCM-YOLO for Lightweight Small Object Detection in Remote Sensing Images |
| title_short | SCM-YOLO for Lightweight Small Object Detection in Remote Sensing Images |
| title_sort | scm yolo for lightweight small object detection in remote sensing images |
| topic | small object detection remote sensing image spatial local information feature fusion attention mechanism |
| url | https://www.mdpi.com/2072-4292/17/2/249 |
| work_keys_str_mv | AT haoqiang scmyoloforlightweightsmallobjectdetectioninremotesensingimages AT weihao scmyoloforlightweightsmallobjectdetectioninremotesensingimages AT meilinxie scmyoloforlightweightsmallobjectdetectioninremotesensingimages AT qiangtang scmyoloforlightweightsmallobjectdetectioninremotesensingimages AT hengshi scmyoloforlightweightsmallobjectdetectioninremotesensingimages AT yixinzhao scmyoloforlightweightsmallobjectdetectioninremotesensingimages AT xiaotenghan scmyoloforlightweightsmallobjectdetectioninremotesensingimages |