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A Fusion Model for Road Detection based on Deep Learning and Fully Connected CRF

机译：基于深度学习和完全连接CRF的道路检测融合模型

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This paper presents a road detection model based on deep learning and fully connected condition random field to fuse image and point cloud data. Firstly, a convolutional neural network is trained to extract multi-scale features of the image. And a point-based deep neural network is trained to extract the multi-scale features of the point cloud. Secondly, the point cloud data is projected to the image plane. The probability maps of image and point cloud in the image plane are obtained by their corresponding multi-scale features, respectively. Thirdly, a Markov-based up-sampling method is used to get a dense height image from a sparse one which is from the point cloud data. A fully connected condition random field model based on the outputs of the two networks and the height image is constructed on the image plane. Finally, the fusion model is effectively solved by the mean-field approximate algorithm. Experiments on KITTI Road dataset show that the proposed model can effectively fuse the image and the point cloud data. Furthermore, the fusion model can also exclude the shadows, road curbs and other interferences in complex scenes.
机译：本文介绍了基于深度学习和完全连接条件随机字段的道路检测模型，熔断图像和点云数据。首先，训练卷积神经网络以提取图像的多尺度特征。培训基于点的深度神经网络以提取点云的多尺度特征。其次，将点云数据投影到图像平面。图像平面中的图像和点云的概率图分别通过它们对应的多尺度特征获得。第三，基于马尔可夫的上采样方法用于从来自点云数据的稀疏稀疏的上采样方法获得密集的高度图像。基于两个网络的输出和高度图像的完全连接的条件随机字段模型在图像平面上构建。最后，通过平均场近似算法有效地解决了融合模型。基蒂路数据集的实验表明，所提出的模型可以有效地熔断图像和点云数据。此外，融合模型也可以排除复杂场景中的阴影，道路遏制和其他干扰。

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