Affiliations: School of Automotive Engineering, Faculty of Vehicle Engineering and Mechanics, Dalian University of Technology, Dalian, China
Correspondence:
[*]
Corresponding author. Jing Lian, School of Automotive Engineering, Faculty of Vehicle Engineering and Mechanics, Dalian University of Technology, Dalian, China. Tel.: +86 15524706235; E-mail: [email protected].
Abstract: In recent years, traditional machine learning algorithms have been gradually replaced by deep learning algorithms. In the field of computer vision, convolutional neural network is considered to be the most successful deep learning model. Based on convolutional neural network, the accuracy of image classification has been greatly improved. In this paper, a method for semantic image segmentation based on convolutional neural network is proposed. Firstly, the disparity map is introduced to improve the segmentation accuracy. To obtain the disparity map with more continuous disparity values, an image smoothing method is used to optimize the disparity map. Then, based on the AlexNet network, a fully convolutional network architecture is proposed for semantic image segmentation. The unpooling operation is employed to restore the extracted features to their original sizes. The experimental results demonstrate that the network can achieve high pixel-wise prediction accuracy and that using RGB-D image as the input of the network can reduce the noisy segmentation outputs.
