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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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2024.3402540