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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Snippet	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... 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...
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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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