车道线分割的空间池化网络

Yanteng Ge, Yangyang Chen, Faxiang Zhang
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引用次数: 0

摘要

本文提出了一种新的车道视觉分割方法——空间池化。在各种复杂的道路环境中,遮挡是最常见也是最困难的场景之一。在现有的文献中,车道线的结构仍然没有考虑遮挡问题。为了更好地解决这一问题,增强车道线的长距离信息融合,首先提出了一种空间池化模块,采用水平池化和垂直池化相结合的方式实现车道线跨方向的池化。该方法可以增强斜向信息融合能力,提高车道线检测性能。在此基础上,提出了一种基于空间池化模块的端到端车道线分割网络。最后,在tussimple车道线数据集上的实验结果验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spatial Pooling Network For Lane Line Segmentation
In this paper, a novel method named spatial pooling is proposed for visual lane line segmentation. Occlusion is one of the most common and difficult scenes among various and complex road environments. In most existing references, the structure of lane line is still not considered for the occlusion problem. To better solve this case and enhance the long-distance information fusion for lane lines, a spatial pooling module is proposed firstly by using a combination of horizontal and vertical pooling to achieve pooling in the direction across the lane line. This method can enhance the information fusion ability in oblique directions and improve the lane line detection performance. Then, for further study, we propose an end-to-end network for lane line segmentation based on the spatial pooling module. Finally, the experimental results on tuSimple lane line dataset verify the effectiveness of our method.
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