Combining single shot multibox detector with transfer learning for ship detection using Sentinel-1 images

Abstract

Synthetic aperture radar (SAR) images are all-weather, all-time, and wide coverage, increasingly used for ship detection to ensure marine surveillance and transportation security. Currently, deep learning has achieved enormous success in object detection with the capability of representation learning. Combining single shot multiBox detector (SSD) with transfer learning is proposed to address ship detection with complex surroundings, such as both ocean and island in this paper. SSD is chosen because its detection accuracy remains high with relative fast speed and transfer learning is chosen because it performs well even with small training datasets. Two types of S SD models integrated with transfer learning, namely, SSD300 and SSD512 with an input size of 300 pixels and 512 pixels in height and width, are applied to ship detection. To evaluate our approach, SAR images dataset acquired by Sentinel-1 are used. Experimental results reveal that compared with SSD300, SSD512 achieves lower false alarm and slighter lower in detection accuracy. These results demonstrate the effectiveness of our method.