2 research outputs found
Ship Instance Segmentation From Remote Sensing Images Using Sequence Local Context Module
The performance of object instance segmentation in remote sensing images has
been greatly improved through the introduction of many landmark frameworks
based on convolutional neural network. However, the object densely issue still
affects the accuracy of such segmentation frameworks. Objects of the same class
are easily confused, which is most likely due to the close docking between
objects. We think context information is critical to address this issue. So, we
propose a novel framework called SLCMASK-Net, in which a sequence local context
module (SLC) is introduced to avoid confusion between objects of the same
class. The SLC module applies a sequence of dilation convolution blocks to
progressively learn multi-scale context information in the mask branch.
Besides, we try to add SLC module to different locations in our framework and
experiment with the effect of different parameter settings. Comparative
experiments are conducted on remote sensing images acquired by QuickBird with a
resolution of and the results show that the proposed method achieves
state-of-the-art performance.Comment: 4 pages, 5 figures, IEEE Geoscience and Remote Sensing Society 201
Fast Single-shot Ship Instance Segmentation Based on Polar Template Mask in Remote Sensing Images
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