An improved mask R-CNN example segmentation algorithm based on RGB-D Online publication date: Fri, 05-Apr-2024
by Gongfa Li; Boao Li; Du Jiang; Bo Tao; Juntong Yun
International Journal of Wireless and Mobile Computing (IJWMC), Vol. 26, No. 3, 2024
Abstract: Combining the characteristics of RGB images and depth images, we propose a reverse fusion instance segmentation algorithm that effectively combines the advantages of RGB and depth information by fusing high-level semantic features with low-level edge detail features. The algorithm uses RGB and depth information in RGB-D images, extracts features from RGB and depth images separately using a Feature Pyramid Network (FPN) and upsamples the high-level features to the same size as the bottommost features. Subsequently, we apply the inverse fusion method to fuse the high-level features with the low-level features. At the same time, a mask optimisation structure is introduced in the mask branch to achieve RGB-D reverse fusion instance segmentation. Experimental results show that this reverse fusion feature model gives satisfactory performance in RGB-D instance segmentation. On the basis of using ResNet-101 as the backbone network, the average accuracy is improved by 10.6% compared with Mask R-CNN without fusing depth information.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Wireless and Mobile Computing (IJWMC):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook