Attention Receptive Pyramid Network for Ship Detection in SAR Images

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing Vol. 13; pp. 2738 - 2756
• Main Authors: Zhao, Yan, Zhao, Lingjun, Xiong, Boli, Kuang, Gangyao
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Aspect ratio; Attention receptive pyramid network; Automatic target recognition; convolutional block attention module (CBAM); Detection; Detectors; Feature extraction; Feature maps; Image detection; Image enhancement; Imaging techniques; Kernel; Machine learning; Marine vehicles; Neural networks; Performance enhancement; Performance evaluation; Proposals; Radar imaging; Radar polarimetry; Ratios; receptive fields block (RFB); SAR (radar); SAR automatic target recognition (SAR ATR); ship detection; Ships; Synthetic aperture radar; synthetic aperture radar (SAR); Target detection; Target recognition
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2020.2997081

With the development of deep learning (DL) and synthetic aperture radar (SAR) imaging techniques, SAR automatic target recognition has come to a breakthrough. Numerous algorithms have been proposed and competitive results have been achieved in detecting different targets. However, due to the influence of various sizes and complex background of ships, detecting multiscale ships in SAR images is still challenging. To solve the problems, a novel network, called attention receptive pyramid network (ARPN), is proposed in this article. ARPN is a two-stage detector and designed to improve the performance of detecting multiscale ships in SAR images by enhancing the relationships among nonlocal features and refining information at different feature maps. Specifically, receptive fields block (RFB) and convolutional block attention module (CBAM) are employed and combined reasonably in attention receptive block to build a top-down fine-grained feature pyramid. RFB, composed of several branches of convolutional layers with specifically asymmetric kernel sizes and various dilation rates, is used for grabbing features of ships with large aspect ratios and enhancing local features with their global dependences. CBAM, which consists of channel and spatial attention mechanisms, is utilized to boost significant information and suppress interference caused by surroundings. To evaluate the effectiveness of ARPN, experiments are conducted on SAR Ship Detection Dataset and two large-scene SAR images. The detection results illustrate that competitive performance has been achieved by our method in comparison with several CNN-based algorithms, e.g., Faster-RCNN, RetinaNet, feature pyramid network, YOLOv3, Dense Attention Pyramid Network, Depth-wise Separable Convolutional Neural Network, High-Resolution Ship Detection Network, and Squeeze and Excitation Rank Faster-RCNN.