FCEGNet: Feature calibration and edge-guided MLP decoder Network for RGB-D semantic segmentation

The references from depth image data provide rich geometric information for traditional RGB semantic segmentation, which effectively improves the performance of semantic segmentation. However, during the process of feature fusion, there are feature biases between RGB features and depth features, which negatively affect cross-modal feature fusion. In this paper, we propose a novel RGB-D network, FCEGNet, consisting of a Feature Calibration Interaction Module (FCIM), a Three-Stream Fusion Extraction Module(TFEM), and an edge-guided MLP decoder. FCIM processes features in different orientations and scales by balancing features across modalities, and exchanges spatial information to allow RGB and depth features to be calibrated and interact with cross-modal features. TFEM performs feature extraction on cross-modal features and combines them with unimodal features to improve the accuracy of enhanced semantic understanding and fine-grained recognition. Dual-stream edge guidance module (DEGM) is designed in the edge-guided MLP decoder to protect the consistency and disparity of cross-modal features while enhancing the edge information and preserving the spatial information, which helps to obtain more accurate segmentation results. Experimental results on the RGB-D dataset show that the proposed FCFGNet is superior and more efficient than several state-of-the-art methods. The generalised validation of FCEGNet on the RGB-T semantic segmentation dataset also achieves better results.