Combining Convolutional-kernel-based Conditional Random Field with Deep Neural Network For Road Detection
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DOI: 10.23977/CNCI2020092
Author(s)
Xiongxuan Huang and Xueyun Chen
Corresponding Author
Xiongxuan Huang
ABSTRACT
Deep learning methods have achieved excellent detection results in various road databases. However, due to some harsh road conditions, these network models still suffer from some abnormal phenomena such as hollowness, unsmooth edges and so on. To address this problem, we improve the road detection network via a novel convolutional-kernel-based conditional random field (CK-CRF), which designs some special convolutional kernels for hollowness scanning. Experiments on two open road datasets show that the proposed method outperforms the-state-of-the-art models by an obvious margin, including the famous deeplab network with a traditional conditional random fields based on fully-connected layers.
KEYWORDS
Automatic pilot; deep learning; conditional random field; convolutional neural network