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Instance segmentation by using mask R-CNN based on feature fusion of RGB and depth images
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Paper Abstract

The instance segmentation for obstacle detection based on machine vision and deep learning is quite important for autonomous driving system. In this paper, a method using the Mask R-CNN based on feature fusion of RGB and depth images for instance segmentation is proposed. It extracts the features of depth image by designing a two-layer NiN network, and uses convolution to realize the feature fusion and dimension reduction of RGB image and depth image. The edge texture in depth image can improve the accuracy of boundary frame positioning. Experimental results on typical benchmark dataset demonstrates the effectiveness of the proposed method, which can improve the segmentation accuracy by 4% and the recall rate by 2%.

Paper Details

Date Published: 27 November 2019
PDF: 6 pages
Proc. SPIE 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence, 113210O (27 November 2019); doi: 10.1117/12.2542243
Show Author Affiliations
Jinyu Sun, Shanghai Univ. (China)
Chengxiong Jin, Shanghai Univ. (China)
Shiwei Ma, Shanghai Univ. (China)


Published in SPIE Proceedings Vol. 11321:
2019 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

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