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首页> 外文会议>2018 13th Annual Conference on System of Systems Engineering >A Fusion Model for Road Detection based on Deep Learning and Fully Connected CRF
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A Fusion Model for Road Detection based on Deep Learning and Fully Connected CRF

机译：基于深度学习和全连接CRF的道路检测融合模型

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摘要

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：本文提出了一种基于深度学习和完全连接条件随机场的道路检测模型，以融合图像和点云数据。首先，训练卷积神经网络以提取图像的多尺度特征。训练了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。图像和点云在图像平面上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稀疏的点云数据中获取密集的高度图像。基于两个网络的输出和高度图像的全连接条件随机场模型构建在图像平面上。最后，通过均值场近似算法有效地解决了融合模型。在KITTI Road数据集上进行的实验表明，该模型可以有效融合图像和点云数据。此外，融合模型还可以排除复杂场景中的阴影，路缘石和其他干扰。

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