Ship detection in SAR Image using YOLOv2
Hamza Mehdi Khan, Cai Yunze · 2018
Ship detection in SAR images is a challenge and has traditionally been carried out using pixel based algorithms such as CFAR, in this paper we use a deep learning based algorithm called YOLOv2 for the aforementioned task and test its performance on three datasets, at different resolution and quality, with two datasets called DS1 and DS2 consisting of over 400 high resolution SAR images with a high ship to image ratio, in which DS1 consists of raw, unfiltered images and the third dataset called DS3 which consists of full scale 5500 single ship images of lower resolution. High detection results are observed which further improve once we train the network by applying transfer learning using weights from DS3 by truncating the last layer of the network and continue training on the combined first two datasets.