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

Jinyu Sun; Chengxiong Jin; Shiwei Ma

doi:10.1117/12.2542243

27 November 2019 Instance segmentation by using mask R-CNN based on feature fusion of RGB and depth images

Jinyu Sun, Chengxiong Jin, Shiwei Ma

Proceedings Volume 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence; 113210O (2019) https://doi.org/10.1117/12.2542243
Event: The Second International Conference on Image, Video Processing and Artifical Intelligence, 2019, Shanghai, China

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%.

Citation Download Citation

Jinyu Sun, Chengxiong Jin, and Shiwei Ma "Instance segmentation by using mask R-CNN based on feature fusion of RGB and depth images", Proc. SPIE 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence, 113210O (27 November 2019); https://doi.org/10.1117/12.2542243

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