Enhanced Cascade R-CNN for Multiscale Object Detection in Dense Scenes From SAR Images

A synthetic aperture radar (SAR) has the characteristics of all-weather and all-time operation, which can achieve uninterrupted detection of targets on the sea surface. Currently, small-sized ship targets in SAR images are difficult to detect in complex backgrounds due to limited pixel information, unclear azimuth information, and weak signals after imaging. This makes it challenging to detect small-scale ship targets in SAR images. In this article, we proposed an enhanced Cascade R-CNN algorithm for detecting small-sized ship targets in complex backgrounds of SAR images. To enhance the multiscale expression ability of the network, we introduce Res2Net with richer multiscale information and establish a spatial enhancement module to increase the weight of the ship target in the aspect map. In addition, a bidirectional feature pyramid structure is constructed to fuse the feature maps output at numerous stages, making the semantic information contained in the feature maps more abundant. To improve the accuracy of the target boundary in dense areas, we introduce a generalized focal loss (GFL) function and improve the output layer prediction network. Experiments conducted on the SAR-Ship-Dataset show that our algorithm achieves precision, recall,
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, and mAP of 92.6%, 92.4%, 92.8%, and 92.5%, respectively. The proposed method exhibits significant advantages over previous advanced methods in ship detection of varying scales in dense scenes. The code will be made available on GitHub.