Most synthetic aperture radar (SAR) ship detectors based on convolutional neural networks
(CNNs) needed preset anchor boxes to object classification and bounding box coordinate …

Benefiting from deep learning, synthetic aperture radar (SAR) ship detection based on
convolutional neural network (CNN) has developed rapidly and corresponding performance …

Recently, in the field of synthetic aperture radar (SAR) ship detection, deep-learning-based
methods have made significant strides in terms of detection accuracy and speed. However …

Syntheticap erture radar (SAR) ship detection in harbors is challenging due to the similar
backscattering of ship targets to surrounding background interference. Prevalent two-stage …

In recent years, with the improvement of synthetic aperture radar (SAR) imaging resolution, it
is urgent to develop methods with higher accuracy and faster speed for ship detection in …

Synthetic aperture radar (SAR) image ship detection has important applications in marine
surveillance. There are two limitations when applying advanced detection methods naively …

Synthetic aperture radar (SAR) images of ships have complex background interference,
multi-scale targets, and irregular distribution characteristics. However, existing mainstream …

Ship detection using synthetic aperture radar (SAR) has been extensively utilized in both the
military and civilian fields. On account of complex backgrounds, large scale variations, small …

Recently, deep learning-based methods for synthetic aperture radar (SAR) ship detection
have made remarkable advancements. However, most existing methods primarily focus on …

More convolutional neural network (CNN) methods are widely utilized for ship detection in
synthetic aperture radar (SAR) images. Nevertheless, there are still some problems that …