论文标题
基于遥感图像中的极地模板蒙版基于极性模板蒙版的快速单发船实例细分
Fast Single-shot Ship Instance Segmentation Based on Polar Template Mask in Remote Sensing Images
论文作者
论文摘要
由于场景和目标的复杂性，遥感图像中的对象检测和实例分割是一项基本且具有挑战性的任务。最新的方法试图考虑实例细分的效率和准确性。为了在本文中改善两者，我们提出了一个单次卷积神经网络结构，该结构在概念上简单明了，同时弥补了单次网络准确性较低的问题。我们的方法用SSS-NET称为基于对象中心的位置以及中心与剪影采样点之间的距离的距离，以不均匀的角度间隔检测目标，从而实现了掩模生成中线条采样的碱性采样。此外，我们根据极坐标中的轮廓模板提出了一个不均匀的极性模板。空中客车船检测挑战数据集和ISAIDSHIPS数据集的实验表明，SSS-NET在船舶实例细分方面具有较强的竞争力和速度。
Object detection and instance segmentation in remote sensing images is a fundamental and challenging task, due to the complexity of scenes and targets. The latest methods tried to take into account both the efficiency and the accuracy of instance segmentation. In order to improve both of them, in this paper, we propose a single-shot convolutional neural network structure, which is conceptually simple and straightforward, and meanwhile makes up for the problem of low accuracy of single-shot networks. Our method, termed with SSS-Net, detects targets based on the location of the object's center and the distances between the center and the points on the silhouette sampling with non-uniform angle intervals, thereby achieving abalanced sampling of lines in mask generation. In addition, we propose a non-uniform polar template IoU based on the contour template in polar coordinates. Experiments on both the Airbus Ship Detection Challenge dataset and the ISAIDships dataset show that SSS-Net has strong competitiveness in precision and speed for ship instance segmentation.
