Abstract
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 manual design and weak in migration capabilities. Based on deep learning, researchers have introduced methods that have shown good performance in order to get better detection results. However, the majority of these methods have a huge network structure and many parameters which greatly restrict the application and promotion. In this paper, a fast and lightweight detection network, namely FASC-Net, is proposed for multi-scale SAR ship detection under complex backgrounds. The proposed FASC-Net is mainly composed of ASIR-Block, Focus-Block, SPP-Block, and CAPE-Block. Specifically, without losing information, Focus-Block is placed at the forefront of FASC-Net for the first down-sampling of input SAR images at first. Then, ASIR-Block continues to down-sample the feature maps and use a small number of parameters for feature extraction. After that, the receptive field of the feature maps is increased by SPP-Block, and then CAPE-Block is used to perform feature fusion and predict targets of different scales on different feature maps. Based on this, a novel loss function is designed in the present paper in order to train the FASC-Net. The detection performance and generalization ability of FASC-Net have been demonstrated by a series of comparative experiments on the SSDD dataset, SAR-Ship-Dataset, and HRSID dataset, from which it is obvious that FASC-Net has outstanding detection performance on the three datasets and is superior to the existing excellent ship detection methods.
Highlights
Synthetic aperture radar (SAR) possesses advantages of reconnaissance and all-weather imaging [1]
It is obvious that the SAR images in the SAR-Ship-Dataset have more noise than those in the SSDD dataset
The quantitative analysis of the detection result of FASC-Net is shown in Figures 16 and 17, and Table 4, from which we can find that despite the noise and island interference, the proposed FASC-Net still achieves relatively high precision (91.1%), recall (92.2%), F1 score (92.1%), and mean average precision (mAP) (96.1%) on the SAR-Ship-Dataset
Summary
Synthetic aperture radar (SAR) possesses advantages of reconnaissance and all-weather imaging [1]. With the development of airborne and spaceborne SAR, it has been widely used in military and civil fields, such as Gaofen-3, Sentinel-1, TerraSAR-X, and Radarsat-2. As a basic maritime task, SAR ship detection has important value in maritime traffic control, fishery management, and maritime emergency rescue [2]. The SAR ship detection field can be roughly divided into two development stages: traditional methods and deep learning-based methods
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