基于HED网络的快速纸张边缘检测方法

HED网络是目前边缘检测性能较好的深度学习网络模型之一，但使用HED网络进行纸张边缘检测时，检测速度较慢，达不到实时性要求。在保证检测精度的前提下，本文提出一种基于HED网络的快速纸张边缘检测方法。将轻量级网络MobileNetV2作为HED主干网，并去除MobileNetV2网络的后2个bottleneck模块和输出通道数较大的卷积层，进一步加快检测速度。此外，去除网络中的池化层，增加一个步长为1的5×5卷积层，提高检测精度。本文制作包含多种情况的纸张数据集MPDS，将本文方法在MPDS上进行训练和测试。实验结果表明，本文提出的模型将ODS和OIS指标分别提高到了0.867和0.876，检测速度可达42.68 FPS，本文方法可以快速准确地进行纸张边缘检测，满足桌面增强系统对纸张检测的要求。

Fast Paper Edge Detection Method Based on HED Network

The Holistically-nested Edge Detection （HED） network is one of the deep learning network models with better edge detection performance at present. However, when the HED is used for edge detection of paper, the detection speed is slow and cannot meet the real-time requirements. On the premise of ensuring the detection accuracy, this paper proposes a fast paper edge detection method based on HED network. This article uses the lightweight network MobileNetV2 as the HED backbone network, and removes the last two bottleneck modules of the MobileNetV2 network and the convolutional layer with a large number of output channels to further accelerate the detection speed. In addition, the pooling layer in the network is removed, and a 5×5 convolutional layer with a step length of 1 is added to improve the detection accuracy. A paper data set MPDS containing a variety of situations is produced, the method proposed in this paper is trained and tested on MPDS. The experimental results show that the proposed model increases the ODS and OIS indicators to 0.867 and 0.876, respectively. The detection speed is 42.68 FPS. The method proposed in this paper can quickly and accurately detect the edge of the paper and meet the requirements of the desktop enhancement system for paper detection.