An Improvement of Line Segment Detection Algorithm Based on Convolutional Neural Network

Hongjian Han, Jiming Zheng
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Abstract

In order to improve the accuracy and integrity of line segment detection, this paper presents an improved algorithm for line segment detection based on convolutional neural network. First, we use a full convolutional neural network to extract endpoints information from the image, the endpoint information includes the position of the endpoints and the direction of the line connected to the endpoints. Then, we use the stacked hourglass network to generate a line heat map of the image, the larger the value of each pixel in the line heat map, the more likely there is a line. Finally, in order to reduce false detection, a method of generating a straight line combining the endpoint information and the line heat map is proposed. Experimental results show that the algorithm effectively reduce line segment detection errors and improve integrity of line segment detection, and it can be applied to tasks such as 3D reconstruction and scene understanding.
基于卷积神经网络的线段检测算法改进
为了提高线段检测的准确性和完整性,本文提出了一种基于卷积神经网络的线段检测改进算法。首先,我们使用全卷积神经网络从图像中提取端点信息,端点信息包括端点的位置和与端点相连的直线的方向。然后,我们使用堆叠沙漏网络生成图像的线热图,线热图中每个像素的值越大,越有可能存在一条线。最后,为了减少误检,提出了一种结合端点信息和直线热图生成直线的方法。实验结果表明,该算法有效降低了线段检测误差,提高了线段检测的完整性,可应用于三维重建和场景理解等任务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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