An Improved Deep Neural Network for Small-Ship Detection in SAR Imagery
Boyi Hu, Hongxia Miao
Abstract
Ship detection by using remote-sensing images based on a synthetic aperture radar (SAR) plays an important role in managing water transportation and marine safety. However, complex background, a small ship size, and low focus on small ships results in difficulties in feature extraction and low detection accuracy. This study proposes a new small SAR ship-detection network. First, a transformer-based dynamic sparse attention module is used to improve the focus and extraction of small ship features. Second, the feature maps are fused with deep layers, and small target-friendly detection heads are used to improve the processing of global information in the network. Third, a more suitable fused loss function is used for small ships to ensure the multi-scale detection capability. Experimental results on publicly available datasets, LS-SSDD_v1.0 and AIR-SARShip-1.0, show that the proposed method effectively improves the detection accuracy of small ships on SAR images without computational burden boost. Compared with other methods based on the convolutional neural network, the proposed method demonstrates better multiscale detection performance.