Offshore and inland river ship detection has been studied on both synthetic aperture radar (SAR) and optical remote sensing imagery. However, the classic ship detection methods based on SAR images can cause a high false alarm ratio and be influenced by the sea surface model, especially on inland rivers and in offshore areas. The classic detection methods based on optical images do not perform well on small and gathering ships. This paper adopts the idea of deep networks and presents a fast regional-based convolutional neural network (R-CNN) method to detect ships from high-resolution remote sensing imagery. First, we choose GaoFen-2 optical remote sensing images with a resolution of 1 m and preprocess the images with a support vector machine (SVM) to divide the large detection area into small regions of interest (ROI) that may contain ships. Then, we apply ship detection algorithms based on a region-based convolutional neural network (R-CNN) on ROI images. To improve the detection result of small and gathering ships, we adopt an effective target detection framework, Faster-RCNN, and improve the structure of its original convolutional neural network (CNN), VGG16, by using multiresolution convolutional features and performing ROI pooling on a larger feature map in a region proposal network (RPN). Finally, we compare the most effective classic ship detection method, the deformable part model (DPM), another two widely used target detection frameworks, the single shot multibox detector (SSD) and YOLOv2, the original VGG16-based Faster-RCNN, and our improved Faster-RCNN. Experimental results show that our improved Faster-RCNN method achieves a higher recall and accuracy for small ships and gathering ships. Therefore, it provides a very effective method for offshore and inland river ship detection based on high-resolution remote sensing imagery.

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An automatic reading of text from an identity (ID) card image has a wide range of social uses. In this paper, we propose a novel method for Chinese text recognition from ID card images taken by cellphone cameras. The paper has two main contributions: (1) A synthetic data engine based on a conditional adversarial generative network is designed to generate million-level synthetic ID card text line images, which can not only retain the inherent template pattern of ID card images but also preserve the diversity of synthetic data. (2) An improved convolutional recurrent neural network (CRNN) is presented to increase Chinese text recognition accuracy, in which DenseNet substitutes VGGNet architecture to extract more sophisticated spatial features. The proposed method is evaluated with more than 7000 real ID card text line images. The experimental results demonstrate that the improved CRNN model trained only on the synthetic dataset can increase the recognition accuracy of Chinese text in cellphone-acquired low-quality images. Specifically, compared with the original CRNN, the average character recognition accuracy (CRA) is increased from 96.87 to 98.57% and the line recognition accuracy (LRA) is increased from 65.92 to 90.10%.

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