In this paper, we introduce HoughToRadon Transform layer, a novel layer designed to improve the speed of neural networks incorporated with Hough Transform to solve semantic image segmentation problems. By placing it after a Hough Transform layer, "inner" convolutions receive modified feature maps with new beneficial properties, such as a smaller area of processed images and parameter space linearity by angle and shift. These properties were not presented in Hough Transform alone. Furthermore, HoughToRadon Transform layer allows us to adjust the size of intermediate feature maps using two new parameters, thus allowing us to balance the speed and quality of the resulting neural network. Our experiments on the open MIDV-500 dataset show that this new approach leads to time savings in document segmentation tasks and achieves state-of-the-art 97.7% accuracy, outperforming HoughEncoder with larger computational complexity.

翻译：本文提出HoughToRadon变换层（HoughToRadon Transform layer），一种旨在提升融合Hough变换的神经网络解决语义图像分割问题速度的新型网络层。将其置于Hough变换层之后，"内部"卷积模块可接收到具有新优势属性的修正特征图，例如处理图像区域缩小以及参数空间在角度与位移方向上的线性特性。这些属性在单独使用Hough变换时无法实现。此外，HoughToRadon变换层通过引入两个新参数，允许我们调节中间特征图的尺寸，从而平衡神经网络的速度与质量。在公开MIDV-500数据集上的实验表明，该方法在文档分割任务中节省了计算时间，并以97.7%的准确率达到了当前最优水平，超越了计算复杂度更高的HoughEncoder。