Abstract
Recently, there has been a high demand for accelerating and improving the detection of automatic cadastral mapping. As this problem is in its starting point, there are many methods of computer vision and deep learning that have not been considered yet. In this paper, we focus on deep learning and provide three geometric post-processing methods that improve the quality of the work. Our framework includes two parts, each of which consists of a few phases. Our solution to this problem uses instance segmentation. In the first part, we use Mask R-CNN with the backbone of pre-trained ResNet-50 on the ImageNet dataset. In the second phase, we apply three geometric post-processing methods to the output of the first part to get better overall output. Here, we also use computational geometry to introduce a new method for simplifying lines which we call it pocket-based simplification algorithm. For evaluating the quality of our solution, we use popular formulas in this field which are recall, precision and F-score. The highest recall we gain is 95 percent which also maintains high Precision of 72 percent. This resulted in an F-score of 82 percent. Implementing instance segmentation using Mask R-CNN with some geometric post-processes to its output gives us promising results for this field. Also, results show that pocket-based simplification algorithms work better for simplifying lines than Douglas-Puecker algorithm.
Abstract (translated)
最近,加速和提高自动地形映射的发现的需求很高。由于这个问题处于开始阶段,还没有考虑许多计算机视觉和深度学习的方法。在本文中,我们关注深度学习,并提供三个几何后处理方法,以提高工作的质量。我们的框架包括两个部分,每个部分包括几个阶段。我们解决这个问题的方法使用实例分割。在第一部分中,我们使用Mask R-CNN,其基础是在ImageNet数据集上预先训练的ResNet-50。在第二部分中,我们应用三个几何后处理方法到第一部分的输出,以获得更好的整体输出。在这里,我们还使用计算几何来介绍一种新的方法,以简化线条,我们称之为“口袋为基础的简化算法”。为了评估我们的解决方案的质量,我们使用该领域流行的公式,即召回、精度和F-score。我们获得的最高的召回率为95%,同时也保持了72%的高精度。这导致了F-score的82%。使用Mask R-CNN加上一些几何后处理将其输出实例分割方法,为我们该领域提供了令人期望的结果。此外,结果显示,口袋为基础的简化算法对于简化线条比Douglas-Puecker算法更有效。
URL
https://arxiv.org/abs/2309.16708
