Abstract
Independent of sunlight and weather conditions, synthetic aperture radar (SAR) imagery is widely applied to detect ships in marine surveillance. This paper proposes a ship target detection algorithm, aiming at the missing and false detection in the situation of dense ship targets in SAR images. Firstly, we used the spatial pyramid pooling (SPP) to enhance the feature extraction capability in different scales. Then, we modified the regression loss function with three factors of center distance, overlap area and length-width ratio to reduce the error of location. Finally, we proposed double threshold soft non maximum suppression (DTSOFT-NMS) to reduce the missing detections for dense ships. The experimental results reveal that our model exhibits excellent performance on the open SAR-ship-dataset and improves average precision (AP) by 6.5% compared with the baseline YOLOv3 model.
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Wang, J., Lin, Y., Zhuang, L., Guo, J. (2020). Towards More Robust Detection for Small and Densely Arranged Ships in SAR Image. In: Peng, Y., et al. Pattern Recognition and Computer Vision. PRCV 2020. Lecture Notes in Computer Science(), vol 12306. Springer, Cham. https://doi.org/10.1007/978-3-030-60639-8_24
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DOI: https://doi.org/10.1007/978-3-030-60639-8_24
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