Small Object Detection Algorithm Based on Improved YOLOv8 for Remote Sensing

Due to the limitations of small targets in remote sensing images such as background noise, poor information, and so on, the results of commonly used detection algorithms in small target detection is not satisfactory. To improve the accuracy of detection results, we develop an improved algorithm base...

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Veröffentlicht in:	IEEE journal of selected topics in applied earth observations and remote sensing Jg. 17; S. 1 - 15
Hauptverfasser:	Yi, Hao, Liu, Bo, Zhao, Bin, Liu, Enhai
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.01.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Ambient noise Attention mechanism Background noise Deep learning Detection Detection algorithms Feature extraction Feature maps Modules Object recognition Optical imaging Optical sensors Parameter modification Real-time systems Remote sensing Target detection Transformers Vision transformer YOLOv8
ISSN:	1939-1404, 2151-1535
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Abstract	Due to the limitations of small targets in remote sensing images such as background noise, poor information, and so on, the results of commonly used detection algorithms in small target detection is not satisfactory. To improve the accuracy of detection results, we develop an improved algorithm based on YOLOv8, called LAR-YOLOv8. First, in the feature extraction network, the local module is enhanced by using the dual-branch architecture attention mechanism, while the vision transformer block is used to maximize the representation of the feature map. Second, an attention-guided bi-directional feature pyramid network is designed to generate more discriminative information by efficiently extracting feature from the shallow network through a dynamic sparse attention mechanism, and adding top-down paths to guide the subsequent network modules for feature fusion. Finally, the RIOU loss function is proposed to avoid the failure of the loss function and improve the shape consistency between the predicted and ground truth box. Experimental results on NWPU VHR-10, RSOD and CARPK datasets verify that LAR-YOLOv8 achieves satisfactory results in terms of mAP (small), mAP, model parameters and FPS, and can prove that our modifications made to the original YOLOv8 model are effective.
AbstractList	Due to the limitations of small targets in remote sensing images, such as background noise, poor information, and so on, the results of commonly used detection algorithms in small target detection is not satisfactory. To improve the accuracy of detection results, we develop an improved algorithm based on YOLOv8, called LAR-YOLOv8. First, in the feature extraction network, the local module is enhanced by using the dual-branch architecture attention mechanism, while the vision transformer block is used to maximize the representation of the feature map. Second, an attention-guided bidirectional feature pyramid network is designed to generate more discriminative information by efficiently extracting feature from the shallow network through a dynamic sparse attention mechanism, and adding top–down paths to guide the subsequent network modules for feature fusion. Finally, the RIOU loss function is proposed to avoid the failure of the loss function and improve the shape consistency between the predicted and ground-truth box. Experimental results on NWPU VHR-10, RSOD, and CARPK datasets verify that LAR-YOLOv8 achieves satisfactory results in terms of mAP (small), mAP, model parameters, and FPS, and can prove that our modifications made to the original YOLOv8 model are effective. Due to the limitations of small targets in remote sensing images such as background noise, poor information, and so on, the results of commonly used detection algorithms in small target detection is not satisfactory. To improve the accuracy of detection results, we develop an improved algorithm based on YOLOv8, called LAR-YOLOv8. First, in the feature extraction network, the local module is enhanced by using the dual-branch architecture attention mechanism, while the vision transformer block is used to maximize the representation of the feature map. Second, an attention-guided bi-directional feature pyramid network is designed to generate more discriminative information by efficiently extracting feature from the shallow network through a dynamic sparse attention mechanism, and adding top-down paths to guide the subsequent network modules for feature fusion. Finally, the RIOU loss function is proposed to avoid the failure of the loss function and improve the shape consistency between the predicted and ground truth box. Experimental results on NWPU VHR-10, RSOD and CARPK datasets verify that LAR-YOLOv8 achieves satisfactory results in terms of mAP (small), mAP, model parameters and FPS, and can prove that our modifications made to the original YOLOv8 model are effective.
Author	Yi, Hao Liu, Bo Zhao, Bin Liu, Enhai
Author_xml	– sequence: 1 givenname: Hao orcidid: 0009-0007-5808-0161 surname: Yi fullname: Yi, Hao organization: National Key Laboratory of Optical Field Manipulation Science and Technology, Chinese Academy of Sciences, Chengdu, China – sequence: 2 givenname: Bo orcidid: 0000-0003-3084-0126 surname: Liu fullname: Liu, Bo organization: National Key Laboratory of Optical Field Manipulation Science and Technology, Chinese Academy of Sciences, Chengdu, China – sequence: 3 givenname: Bin surname: Zhao fullname: Zhao, Bin organization: National Key Laboratory of Optical Field Manipulation Science and Technology, Chinese Academy of Sciences, Chengdu, China – sequence: 4 givenname: Enhai surname: Liu fullname: Liu, Enhai organization: National Key Laboratory of Optical Field Manipulation Science and Technology, Chinese Academy of Sciences, Chengdu, China
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Cites_doi	10.1109/CVPR.2019.00075 10.1145/3505244 10.3390/rs12152501 10.1080/01431161.2020.1811422 10.1016/j.neucom.2022.07.042 10.1109/ICCV.2017.74 10.1109/TGRS.2017.2778300 10.1109/CVPR.2017.690 10.1109/CVPR.2016.91 10.1109/TIV.2023.3282567 10.1109/JSTARS.2021.3087555 10.1007/978-3-030-58555-6_16 10.1109/TCYB.2021.3095305 10.1109/TGRS.2016.2601622 10.3390/rs12223750 10.3390/rs13112171 10.1109/ICCV48922.2021.00061 10.3390/rs14040871 10.1609/aaai.v34i07.6999 10.1109/JSTARS.2020.3005403 10.1371/journal.pone.0259283 10.1109/CVPR52688.2022.00475 10.3390/rs13071311 10.1109/TGRS.2016.2645610 10.1109/TPAMI.2022.3152247 10.1109/JSTARS.2022.3206399 10.1016/j.isprsjprs.2019.11.023 10.1109/ICCV.2017.322 10.1016/j.compeleceng.2022.108490 10.1109/TGRS.2023.3258666 10.1109/TPAMI.2019.2956516 10.1109/JSTARS.2022.3140776 10.1007/978-3-319-10602-1_48 10.1109/JSTARS.2022.3148139 10.1109/LGRS.2018.2813094 10.1109/CVPR52688.2022.01058 10.1109/TPAMI.2015.2437384 10.1109/ICCV.2019.00667 10.1109/CVPR52729.2023.00995 10.1109/icip49359.2023.10222333 10.1109/CVPR52729.2023.00721 10.1109/LGRS.2019.2912582 10.1109/tpami.2016.2577031 10.1109/JSTARS.2022.3176141 10.1109/ICCV.2017.446 10.1109/ICCVW54120.2021.00312 10.1117/1.JEI.31.4.043049 10.1016/j.jag.2021.102456 10.1109/ACCESS.2023.3233964
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References	ref15 Li (ref14) 2022 ref53 ref52 ref10 ref54 ref17 Bochkovskiy (ref12) 2020 ref16 ref19 Fan (ref39) 2023 ref18 ref51 ref50 Jocher (ref13) 2020 ref46 ref45 ref48 ref47 ref42 ref41 ref44 ref43 ref49 ref8 ref7 ref9 ref4 ref3 ref6 ref5 Redmon (ref11) 2018 ref40 ref35 ref34 ref37 ref36 ref31 ref30 ref33 ref32 ref2 ref1 ref38 ref24 ref23 ref26 ref25 ref20 ref22 ref21 ref28 ref27 ref29
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SubjectTerms	Algorithms Ambient noise Attention mechanism Background noise Deep learning Detection Detection algorithms Feature extraction Feature maps Modules Object recognition Optical imaging Optical sensors Parameter modification Real-time systems Remote sensing Target detection Transformers Vision transformer YOLOv8
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Title	Small Object Detection Algorithm Based on Improved YOLOv8 for Remote Sensing
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