Accurate and efficient extraction of road marking plays an important role in road transportation engineering, automotive vision, and automatic driving. In this article, we proposed a dense feature pyramid network (DFPN)-based deep learning model, by considering the particularity and complexity of road marking. The DFPN concatenated its shallow feature channels with deep feature channels so that the shallow feature maps with high resolution and abundant image details can utilize the deep features. Thus, the DFPN can learn hierarchical deep detailed features. The designed deep learning model was trained end to end for road marking instance extraction with mobile laser scanning (MLS) point clouds. Then, we introduced the focal loss function into the optimization of deep learning model in road marking segmentation part, to pay more attention to the hard-classified samples with a large extent of background. In the experiments, our method can achieve better results than state-of-the-art methods on instance segmentation of road markings, which illustrated the advantage of the proposed method.

中文翻译：

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基于密集特征金字塔网络的深度学习模型，用于使用MLS点云的道路标记实例细分

准确，有效地提取道路标记在道路运输工程，汽车视觉和自动驾驶中起着重要作用。在本文中，我们考虑了道路标记的特殊性和复杂性，提出了一种基于密集特征金字塔网络（DFPN）的深度学习模型。DFPN将其浅层特征通道与深层特征通道连接在一起，以便具有高分辨率和丰富图像细节的浅层特征图可以利用深层特征。因此，DFPN可以学习分层的深层详细功能。设计的深度学习模型经过端到端培训，可使用移动激光扫描（MLS）点云提取道路标记实例。然后，将焦点损失函数引入道路标记分割部分的深度学习模型优化中，更多地关注那些背景较难分类的样本。在实验中，我们的方法在道路标线实例分割方面比最新方法取得了更好的效果，这说明了该方法的优势。