Ship is an Src homology 2 domain containing inositol polyphosphate 5-phosphatase which has been implicated as an important signaling molecule in hematopoietic cells. In B cells, Ship becomes associated with Fcgamma receptor IIB (FcgammaRIIB), a low affinity receptor for the Fc portion of immunoglobulin (Ig)G, and is rapidly tyrosine phosphorylated upon B cell antigen receptor (BCR)-FcgammaRIIB ...

The detection of small ships in polarimetric synthetic aperture radar (PolSAR) images is still a topic for further investigation. Recently, patch techniques, such as superpixel-level detection, have stimulated wide interest because they can use the information contained similarities among neighboring pixels. In this article, we propose novel neighborhood covariance matrix (NPCM) to detect PolSA...

Abstract An improved deep learning neural model YOLOv5-DN based on YOLOv5 is proposed for marine ship detection and classification in the area of harbours heavy traffic waterways. The CSP-DarkNet module replaced by CSP-DenseNet to promote accuracy target model. Sample ships data set are divided into six classes: ore carriers, general cargo ships, bulk container passenger fishing meet needs area...

Ship detection in remote sensing images plays a crucial role various applications and has drawn increasing attention recent years. However, existing arbitrary-oriented ship methods are generally developed on set of predefined rotated anchor boxes. These boxes not only lead to inaccurate angle predictions but also introduce extra hyper-parameters high computational cost. Moreover, the prior know...

目的 舰船舷号检测识别是海面态势感知的关键技术，精准的舷号检测识别对海洋权益保护具有重要意义。但目前没有公开数据提供支持。为此，本文先构建了一个真实场景下的稀疏舰船舷号数据集（sparse ship hullnumber dataset in real scene，SSHN-RS），包含3 004幅舰船图像，共计11 328个舷号字符，覆盖了多国、各类、水平、倾斜、背景简单、背景复杂、光线不佳和被遮挡的舰船舷号样本，是一个具有挑战性的数据集。基于SSHN-RS，开展舰船舷号检测识别研究，其主要难点在于：1）样本稀疏，模型容易过拟合；2）舷号字符分布密集，网络难以充分提取各字符特征；3）部分字符存在嵌套区域和相似区域，网络会识别出大量冗余结果。针对上述难点，提出了一种基于多视角渐进式上下文解耦的舰船舷号检测识别算法。方法 首先，引入一个固定中心和最大化面积的随机透视变换技术，在不增加样...

With the successful application of convolutional neural network (CNN), significant progress has been made by CNN-based ship detection methods. However, they often face considerable difficulties when applied to a new domain where imaging condition changes significantly. Although training with two domains together can solve this problem some extent, large shift will lead sub-optimal feature repre...