A Lightweight Faster R-CNN for Ship Detection in SAR Images

• Published in: IEEE geoscience and remote sensing letters Vol. 19; pp. 1 - 5
• Main Authors: Li, Yiding, Zhang, Shunsheng, Wang, Wen-Qin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Convolution; Decision making; Deep learning; Detection; faster algorithm; faster region-based convolutional neural network (R-CNN); Feature extraction; Learning algorithms; Lightweight; lightweight faster R-CNN; Localization; Machine learning; Marine vehicles; Methods; Military applications; Neural networks; Object recognition; Radar detection; Radar polarimetry; Relays; SAR (radar); ship detection; Synthetic aperture radar; synthetic aperture radar (SAR); Target detection; Task analysis
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2020.3038901

Deep learning algorithms have been widely utilized for synthetic aperture radar (SAR) target detection. Nevertheless, the traditional feature extraction methods and deep learning methods achieve improved ship detection accuracy at a cost of increased complexity and lower detection speed. As detection speed also is meaningful, especially in real-time maritime rescue and emergency military decision-making applications, we propose a new framework of faster region-based convolutional neural network (R-CNN) detection method to handle this problem. A new lightweight basic network with feature relay amplification and multiscale feature jump connection structure is designed to extract the features of each scale target in the SAR images, so as to improve its recognition and localization task network. Moreover, the K-Means method is used to obtain the distribution of the target scale, which enables to select more appropriate preset anchor boxes to reduce the difficulty of network learning. Finally, RoIAlign instead of region of interest (RoI) Pooling is used to reduce the quantization error during positioning. Experimental results show that the detection performance of the proposed method achieves 0.898 average precision (AP), which is 2.78% better than the conventional Faster R-CNN and 800% faster detection speed.