Convolution neural network (CNN)-based detectors have shown great performance on ship
detections of synthetic aperture radar (SAR) images. However, the performance of current …

The problem of multiscale ship detection in synthetic aperture radar (SAR) images has
received much attention with the development of deep convolutional neural networks …

With the development of artificial intelligence (AI) and synthetic aperture radar (SAR)
technology, SAR ship target automatic detection has made significant progress. However …

Ship detection from synthetic aperture radar (SAR) imagery plays a significant role in global
marine surveillance. However, a desirable performance is rarely achieved when detecting …

With the development of synthetic aperture radar (SAR) imaging and deep learning, SAR
ship detection based on convolutional neural networks (CNNs) has been extensively …

It is very difficult to detect multi-scale synthetic aperture radar (SAR) ships, especially under
complex backgrounds. Traditional constant false alarm rate methods are cumbersome in …

Recently, deep-learning methods have been successfully applied to the ship detection in the
synthetic aperture radar (SAR) images. It is still a great challenge to detect multiscale SAR …

Recently, methods based on deep learning have been successfully applied to ship detection
for synthetic aperture radar (SAR) images. However, most current ship detection networks …

Synthetic aperture radar (SAR) images have been widely used for ship monitoring. The
traditional methods of SAR ship detection are difficult to detect small scale ships and avoid …

With the booming of Convolutional Neural Networks (CNNs), CNNs such as VGG-16 and
ResNet-50 widely serve as backbone in SAR ship detection. However, CNN based …