In this paper, we introduce a new large-scale dataset of ships, called SeaShips, which is designed for training and evaluating ship object detection algorithms. The dataset currently consists of 31 455 images and covers six common ship types (ore carrier, bulk cargo carrier, general cargo ship, container ship, fishing boat, and passenger ship). All of the images are from about 10 080 real-world video segments, which are acquired by the monitoring cameras in a deployed coastline video surveillance system. They are carefully selected to mostly cover all possible imaging variations, for example, different scales, hull parts, illumination, viewpoints, backgrounds, and occlusions. All images are annotated with ship-type labels and high-precision bounding boxes. Based on the SeaShips dataset, we present the performance of three detectors as a baseline to do the following: 1) elementarily summarize the difficulties of the dataset for ship detection; 2) show detection results for researchers using the dataset; and 3) make a comparison to identify the strengths and weaknesses of the baseline algorithms. In practice, the SeaShips dataset would hopefully advance research and applications on ship detection.

Building extraction from high-resolution remote sensing images is of great significance in urban planning, population statistics, and economic forecast. However, automatic building extraction from high-resolution remote sensing images remains challenging. On the one hand, the extraction results of buildings are partially missing and incomplete due to the variation of hue and texture within a building, especially when the building size is large. On the other hand, the building footprint extraction of buildings with complex shapes is often inaccurate. To this end, we propose a new deep learning network, termed Building Residual Refine Network (BRRNet), for accurate and complete building extraction. BRRNet consists of such two parts as the prediction module and the residual refinement module. The prediction module based on an encoder–decoder structure introduces atrous convolution of different dilation rates to extract more global features, by gradually increasing the receptive field during feature extraction. When the prediction module outputs the preliminary building extraction results of the input image, the residual refinement module takes the output of the prediction module as an input. It further refines the residual between the result of the prediction module and the real result, thus improving the accuracy of building extraction. In addition, we use Dice loss as the loss function during training, which effectively alleviates the problem of data imbalance and further improves the accuracy of building extraction. The experimental results on Massachusetts Building Dataset show that our method outperforms other five state-of-the-art methods in terms of the integrity of buildings and the accuracy of complex building footprints.

Rainy weather is a challenge for many vision-oriented tasks ( e.g. , object detection and segmentation), which causes performance degradation. Image deraining is an effective solution to avoid performance drop of downstream vision tasks. However, most existing deraining methods either fail to produce satisfactory restoration results or cost too much computation. In this work, considering both effectiveness and efficiency of image deraining, we propose a progressive coupled network (PCNet) to well separate rain streaks while preserving rain-free details. To this end, we investigate the blending correlations between them and particularly devise a novel coupled representation module (CRM) to learn the joint features and the blending correlations. By cascading multiple CRMs, PCNet extracts the hierarchical features of multi-scale rain streaks, and separates the rain-free content and rain streaks progressively. To promote computation efficiency, we employ depth-wise separable convolutions and a U-shaped structure, and construct CRM in an asymmetric architecture to reduce model parameters and memory footprint. Extensive experiments are conducted to evaluate the efficacy of the proposed PCNet in two aspects: (1) image deraining on several synthetic and real-world rain datasets and (2) joint image deraining and downstream vision tasks ( e.g. , object detection and segmentation). Furthermore, we show that the proposed CRM can be easily adopted to similar image restoration tasks including image dehazing and low-light enhancement with competitive performance. The source code is available at https://github.com/kuijiang0802/PCNet .

During COVID-19 coronavirus epidemic, almost everyone wears a mask to prevent the spread of virus. It raises a problem that the traditional face recognition model basically fails in the scene of face-based identity verification, such as security check, community visit check-in, etc. Therefore, it is imminent to boost the performance of masked face recognition. Most recent advanced face recognition methods are based on deep learning, which heavily depends on a large number of training samples. However, there are presently no publicly available masked face recognition datasets, especially real ones. To this end, this work proposes three types of masked face datasets, including Masked Face Detection Dataset (MFDD), Real-world Masked Face Recognition Dataset (RMFRD) and Synthetic Masked Face Recognition Dataset (SMFRD). Besides, we conduct benchmark experiments on these three datasets for reference. As far as we know, we are the first to publicly release large-scale masked face recognition datasets that can be downloaded for free at https://github.com/X-zhangyang/Real-World-Masked-Face-Dataset..

Real-time detection of inshore ships plays an essential role in the efficient monitoring and management of maritime traffic and transportation for port management. Current ship detection methods which are mainly based on remote sensing images or radar images hardly meet real-time requirement due to timeliness of image acquisition. In this paper, we propose to use visual images captured by an on-land surveillance camera network to achieve real-time detection. However, due to the complex background of visual images and the diversity of ship categories, the existing convolution neural network (CNN) based methods are either inaccurate or slow. To achieve high detection accuracy and real-time performance simultaneously, we propose a saliency-aware CNN framework for ship detection, comprising comprehensive ship discriminative features, such as deep feature, saliency map and coastline prior. This model uses CNN to predict the category and the position of ships, and uses the global contrast based salient region detection to correct the location. We also extract coastline information and respectively incorporate it into CNN and saliency detection to obtain more accurate ship locations. We implement our model on Darknet under CUDA 8.0 and CUDNN V5 and use a real-world visual image dataset for training and evaluation. The experimental results show that our model outperforms representative counterparts (Faster R-CNN, SSD, and YOLOv2) in terms of accuracy and speed.