U-Net Based Road Area Guidance for Crosswalks Detection from Remote Sensing Images
U-Net Based Road Area Guidance for Crosswalks Detection from Remote Sensing Images
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DOI：
10.1080/07038992.2021.1894915
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发表时间：
2021-02-26
影响因子：
2.6
通讯作者：
Wang, Cheng
中科院分区：
文献类型：
--
作者：
Chen, Ziyi;Luo, Ruixiang;Wang, Cheng
由于人行横道在路网中分布广泛，发现受损的人行横道标志通常会被长期延误，这可能会使人行横道行人处于危险之中。为了降低人行横道的维修成本，提高发现损坏人行横道的速度，本文利用遥感图像对人行横道进行自动检测。检测结果可用于人行横道的进一步检测。然而，从遥感图像中检测人行横道受到了许多其他类型地面目标的严重干扰。此外，用于从遥感图像中检测人行横道的研究也鲜有公开可用的数据集。为克服上述问题，本研究为人行横道检测的研究提供了一个开放的数据集。为了提高检测的稳健性，我们提出了一种基于U网的道路区域引导的人行横道检测框架。首先，我们使用CNN模型来检测人行横道。然后，使用U网提取潜在的道路区域。第三，提出了一种将检测置信度和潜在道路区域指导相结合的混合分类策略，用于最终的人行横道检测。实验结果表明，基于区域引导的人行横道检测方法是有效的，能够提高检测性能。
Due to the wide distribution of crosswalks over the road nets, the finding of impaired crosswalk marks is usually long-time delayed, which may put crosswalk pedestrians into danger. To reduce the repairing cost and improve the finding speed of damaged crosswalks, this paper uses remote sensing images to automatically detect crosswalks. The detection results can be used for further examination of crosswalks. However, the detection of crosswalks from remote sensing images suffers from serious interferes of many other kinds of ground targets. Besides, there are rare openly available datasets for the research of crosswalk detection from remote sensing images. To conquer the above problems, this study provides an openly available dataset for the research of crosswalk detection. To improve the robustness, we propose a crosswalk detection framework which uses a U-Net based road area guidance. First, we use CNN models to detect crosswalks. Then, we use U-Net to extract potential road areas. Third, we propose a mixture classification strategy which combines the detection confidence and potential road area guidance for final crosswalk detection. Experimental results show that the road area guidance for crosswalks' detection is effective and can improve the detection performance.