BENCHMARK FOR ARBITRARY-ORIENTED SAR SHIP DETECTION

A growing number of researchers have begun to use arbitrary-oriented detectors to detect ships. Because in the remote sensing dataset, ships are shown to be narrow and dense. Most of the deep learning methods used in synthetic aperture radar (SAR) ship detection are the same as or variants of those of optical remote sensing. However, the experimental settings of different papers are different. Therefore, various arbitrary-oriented target detectors can not be compared fairly on the SAR dataset. To solve this problem, we developed an arbitrary-oriented SAR ship detection benchmark, which provides strong baselines and state-of-the-art methods in rotation detection. All benchmark methods are tested on RSSDD datasets, and the code is publicly released at https://github.com/open-mmlab/mmrotate. Meanwhile, this paper further explores the role of ImageNet pretrained weight in SAR target detection and tries to train the SAR pretrained weight through unsupervised learning.