IA-CIOU: An Improved IOU Bounding Box Loss Function for SAR Ship Target Detection Methods

Ship detection in synthetic aperture radar (SAR) images is crucial in both civilian and military fields, offering extensive application prospects. Nonetheless, owing to the distinctive characteristics of SAR imaging, this task confronts numerous challenges. Specifically, ships with high aspect ratio...

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Veröffentlicht in:	IEEE journal of selected topics in applied earth observations and remote sensing Jg. 17; S. 10569 - 10582
Hauptverfasser:	Huang, Pingping, Tian, Shihao, Su, Yun, Tan, Weixian, Dong, Yifan, Xu, Wei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Aspect ratio Classification algorithms Datasets Deep learning Feature extraction Intersection over union (IOU) loss function Marine vehicles Military applications Radar detection Radar imaging Radar polarimetry SAR (radar) ship detection Synthetic aperture radar synthetic aperture radar (SAR) images Target detection Task analysis YOLO YOLOv5
ISSN:	1939-1404, 2151-1535
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Abstract	Ship detection in synthetic aperture radar (SAR) images is crucial in both civilian and military fields, offering extensive application prospects. Nonetheless, owing to the distinctive characteristics of SAR imaging, this task confronts numerous challenges. Specifically, ships with high aspect ratios, dense arrangements and small sizes in complex environments frequently yield in suboptimal positioning effects, consequently impacting detection performance. In response to the challenges in ship target detection, this article introduces a novel approach, termed Inner-alpha-CIOU (IA-CIOU), that relies on an enhanced intersection over union (IOU). Primarily, the method introduces Inner IOU, which effectively regulates generation of auxiliary bounding boxes through scale factor r . This ensures a better fit for dimensions of ship target frames, thereby enhancing target detection performance as well as expediting model convergence. Subsequently, this method introduces Alpha IOU, enhancing robustness of small-size ship targets in complex backgrounds by adjusting α . This allows the detector to achieve greater flexibility in ship regression accuracy. Following numerous experimental validations, proposed algorithm consistently outperforms on both SAR-Ship-Dataset, MSAR-1.0 dataset, and SAR ship detection dataset (SSDD) dataset. This groundbreaking innovation not only possesses immeasurable practical worth, but also introduces a fresh perspective together with enlightening insights for future research efforts.
AbstractList	Ship detection in synthetic aperture radar (SAR) images is crucial in both civilian and military fields, offering extensive application prospects. Nonetheless, owing to the distinctive characteristics of SAR imaging, this task confronts numerous challenges. Specifically, ships with high aspect ratios, dense arrangements and small sizes in complex environments frequently yield in suboptimal positioning effects, consequently impacting detection performance. In response to the challenges in ship target detection, this article introduces a novel approach, termed Inner-alpha-CIOU (IA-CIOU), that relies on an enhanced intersection over union (IOU). Primarily, the method introduces Inner IOU, which effectively regulates generation of auxiliary bounding boxes through scale factor r . This ensures a better fit for dimensions of ship target frames, thereby enhancing target detection performance as well as expediting model convergence. Subsequently, this method introduces Alpha IOU, enhancing robustness of small-size ship targets in complex backgrounds by adjusting α . This allows the detector to achieve greater flexibility in ship regression accuracy. Following numerous experimental validations, proposed algorithm consistently outperforms on both SAR-Ship-Dataset, MSAR-1.0 dataset, and SAR ship detection dataset (SSDD) dataset. This groundbreaking innovation not only possesses immeasurable practical worth, but also introduces a fresh perspective together with enlightening insights for future research efforts.
Author	Tian, Shihao Su, Yun Tan, Weixian Huang, Pingping Xu, Wei Dong, Yifan
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SubjectTerms	Algorithms Aspect ratio Classification algorithms Datasets Deep learning Feature extraction Intersection over union (IOU) loss function Marine vehicles Military applications Radar detection Radar imaging Radar polarimetry SAR (radar) ship detection Synthetic aperture radar synthetic aperture radar (SAR) images Target detection Task analysis YOLO YOLOv5
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Title	IA-CIOU: An Improved IOU Bounding Box Loss Function for SAR Ship Target Detection Methods
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