Remote Sensing Object Detection Based on Convolution and Swin Transformer
Remote sensing object detection is an essential task for surveying the earth. It is challenging for the target detection algorithm in natural scenes to obtain satisfactory detection results in remote sensing images. In this paper, the RAST-YOLO (You only look once with Regin Attention and Swin Trans...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10103543/ |
| _version_ | 1797840116115308544 |
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| author | Xuzhao Jiang Yonghong Wu |
| author_facet | Xuzhao Jiang Yonghong Wu |
| author_sort | Xuzhao Jiang |
| collection | DOAJ |
| description | Remote sensing object detection is an essential task for surveying the earth. It is challenging for the target detection algorithm in natural scenes to obtain satisfactory detection results in remote sensing images. In this paper, the RAST-YOLO (You only look once with Regin Attention and Swin Transformer) algorithm is proposed to address the problems of remote sensing object detection, such as significant differences in target scales, complex backgrounds, and tightly arranged small-size targets. To increase the information interaction range of the feature map, make full use of the background information of the object, and improve the detection accuracy of the object with a complex background, the Regin Attention (RA) mechanism combined with Swin Transformer as the backbone is proposed to extract features. To improve the detection accuracy of small objects, the C3D module is used to fuse deep and shallow semantic information and optimize the multi-scale problem of remote sensing targets. To evaluate the performance of RAST-YOLO, extensive experiments are performed on DIOR and TGRS-HRRSD datasets. The experimental results show that RAST achieves state-of-the-art detection accuracy with high efficiency and robustness. Specifically, compared with the baseline network, the mean average precision (mAP) of detection results is improved by 5% and 2.3% on DIOR and TGRS-HRRSD datasets, respectively, which demonstrates RAST-YOLO is effective and superior. Moreover, the lightweight structure of RAST-YOLO can ensure the real-time detection speed and obtain excellent detection results. |
| first_indexed | 2024-04-09T16:09:21Z |
| format | Article |
| id | doaj.art-ffee313b737b4df9ae64553ce83d5d11 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-09T16:09:21Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-ffee313b737b4df9ae64553ce83d5d112023-04-24T23:00:35ZengIEEEIEEE Access2169-35362023-01-0111386433865610.1109/ACCESS.2023.326743510103543Remote Sensing Object Detection Based on Convolution and Swin TransformerXuzhao Jiang0https://orcid.org/0000-0002-9264-6989Yonghong Wu1Department of Statistics, Wuhan University of Technology, Wuhan, ChinaDepartment of Statistics, Wuhan University of Technology, Wuhan, ChinaRemote sensing object detection is an essential task for surveying the earth. It is challenging for the target detection algorithm in natural scenes to obtain satisfactory detection results in remote sensing images. In this paper, the RAST-YOLO (You only look once with Regin Attention and Swin Transformer) algorithm is proposed to address the problems of remote sensing object detection, such as significant differences in target scales, complex backgrounds, and tightly arranged small-size targets. To increase the information interaction range of the feature map, make full use of the background information of the object, and improve the detection accuracy of the object with a complex background, the Regin Attention (RA) mechanism combined with Swin Transformer as the backbone is proposed to extract features. To improve the detection accuracy of small objects, the C3D module is used to fuse deep and shallow semantic information and optimize the multi-scale problem of remote sensing targets. To evaluate the performance of RAST-YOLO, extensive experiments are performed on DIOR and TGRS-HRRSD datasets. The experimental results show that RAST achieves state-of-the-art detection accuracy with high efficiency and robustness. Specifically, compared with the baseline network, the mean average precision (mAP) of detection results is improved by 5% and 2.3% on DIOR and TGRS-HRRSD datasets, respectively, which demonstrates RAST-YOLO is effective and superior. Moreover, the lightweight structure of RAST-YOLO can ensure the real-time detection speed and obtain excellent detection results.https://ieeexplore.ieee.org/document/10103543/Remote sensing imagesobject detectionattention mechanismswin transformermulti-scale features |
| spellingShingle | Xuzhao Jiang Yonghong Wu Remote Sensing Object Detection Based on Convolution and Swin Transformer IEEE Access Remote sensing images object detection attention mechanism swin transformer multi-scale features |
| title | Remote Sensing Object Detection Based on Convolution and Swin Transformer |
| title_full | Remote Sensing Object Detection Based on Convolution and Swin Transformer |
| title_fullStr | Remote Sensing Object Detection Based on Convolution and Swin Transformer |
| title_full_unstemmed | Remote Sensing Object Detection Based on Convolution and Swin Transformer |
| title_short | Remote Sensing Object Detection Based on Convolution and Swin Transformer |
| title_sort | remote sensing object detection based on convolution and swin transformer |
| topic | Remote sensing images object detection attention mechanism swin transformer multi-scale features |
| url | https://ieeexplore.ieee.org/document/10103543/ |
| work_keys_str_mv | AT xuzhaojiang remotesensingobjectdetectionbasedonconvolutionandswintransformer AT yonghongwu remotesensingobjectdetectionbasedonconvolutionandswintransformer |