An improved mask R-CNN example segmentation algorithm based on RGB-D

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. Copyright © 2024 Inderscience Enterprises Ltd.