Abstract
Road detection from the perspective of moving vehicles is a challenging issue in autonomous driving. Recently, many deep learning methods spring up for this task because they can extract high-level local features to find road regions from raw RGB data, such as Convolutional Neural Networks (CNN) and Fully Convolutional Networks (FCN). However, how to detect the boundary of road accurately is still an intractable problem. In this paper, we propose a siamesed fully convolutional networks (named as ``s-FCN-loc''), which is able to consider RGB-channel images, semantic contours and location priors simultaneously to segment road region elaborately. To be specific, the s-FCN-loc has two streams to process the original RGB images and contour maps respectively. At the same time, the location prior is directly appended to the siamesed FCN to promote the final detection performance. Our contributions are threefold: (1) An s-FCN-loc is proposed that learns more discriminative features of road boundaries than the original FCN to detect more accurate road regions; (2) Location prior is viewed as a type of feature map and directly appended to the final feature map in s-FCN-loc to promote the detection performance effectively, which is easier than other traditional methods, namely different priors for different inputs (image patches); (3) The convergent speed of training s-FCN-loc model is 30\% faster than the original FCN, because of the guidance of highly structured contours. The proposed approach is evaluated on KITTI Road Detection Benchmark and One-Class Road Detection Dataset, and achieves a competitive result with state of the arts.
Abstract (translated)
从行驶车辆的角度进行道路检测是自动驾驶中一个具有挑战性的问题。近年来，许多深层次学习方法出现在这项任务中，因为它们可以从原始的RGB数据中提取高层次的局部特征，如卷积神经网络（CNN）和完全卷积网络（FCN）。然而，如何准确地检测道路边界仍然是一个棘手的问题。本文提出了一种连体的完全卷积网络（称为“S-FCN-LOC”），它可以同时考虑RGB通道图像、语义轮廓和位置先验，对道路区域进行详细的分割。具体来说，S-FCN-LOC有两个流分别处理原始的RGB图像和轮廓图。同时，预先定位直接附加到连体FCN上，以提高最终检测性能。我们的贡献有三个方面：（1）提出了一种S-FCN-LOC，它比原来的FCN学习更多的道路边界识别特征，以检测更准确的道路区域；（2）将先验位置作为一种特征图，直接附加到S-FCN-LOC中的最终特征图中，以有效地提高检测性能，从而提高检测效率。由于S-FCN-LOC模型具有较高的轮廓线导引能力，使得训练S-FCN-LOC模型的收敛速度比原始FCN模型快30%。在基蒂道路检测基准和一级道路检测数据集上对该方法进行了评价，取得了与现有技术相竞争的结果。
URL
https://arxiv.org/abs/1905.01575
