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Depth information can contribute to the semantic segmentation of scenes from red–green–blue (RGB) images. Therefore, the amount of information that can be obtained from RGB and RGB-depth (RGB-D) images is significantly greater for this task. However, RGB and RGB-D modalities are different in terms of object representation. Features that are extracted from these modalities and fused effectively are key to scene semantic segmentation. In addition, complete segmentation requires the fusion of multiscale features to unify global information. However, existing approaches primarily use multiscale features for sequential integration. This study introduces a cross-modal and progressive feature fusion network (CMPFFNet) for semantic segmentation of indoor scenes in RGB-D images. First, a multimodal adaptive alignment fusion (MAAF) module based on an attention mechanism is introduced. This module aligns the two modal channels by additive attention and then computes the spatial similarity between the two modalities based on the dot product to incorporate the complementary information of the depth modality into the RGB modality. In addition, a reverse attention augmentation (RAA) module is introduced to augment the more abstract high-level features for two adjacent multilevel features using the concrete semantic information of the lower-level features in them. After augmenting the extracted multilevel features, a multilevel feature progressive fusion (MFPF) module is deployed; this module sequentially fuses the neighboring two features progressively with emphasis on the spatial semantics. The network uses the Segformer network with high performance as a backbone in multiple computer vision tasks to enhance the segmentation capability. Experimental results obtained from two publicly available datasets of indoor scenes reveal that the proposed CMPFFNet outperforms existing models in semantic segmentation of indoor scenes of RGB-D images. Note to Practitioners—This study introduces a cross-modal and progressive feature fusion network (CMPFFNet) for indoor scene semantic segmentation in RGB-D images. The complementary information of the depth modality is incorporated into the RGB modality in both channel and spatial forms to form a discriminative representation for easy segmentation. A multilevel feature aggregation decoder is proposed to predict the results of semantic segmentation of scenes. The network uses the Segformer network with high performance as a backbone in multiple computer vision tasks to enhance the segmentation capability.

Owing to the development of convolutional neural networks (CNNs), the detection of defects on rail surfaces has significantly improved. Although existing methods achieve good results, they incur huge computational and parameter costs associated with CNNs. The usual approach to this problem is to design lightweight models that meet the needs of real-world applications; however, the performance is often compromised. To address the aforementioned problems, we designed a dual semantic approximation network via knowledge distillation (DSANet-KD, a student model with knowledge distillation) for rail surface defect detection; it focuses on both foreground and background knowledge and obtains more accurate prediction results. This model comprises an adaptive 3D spatial integration module, feature-optimization decoding module, and dual semantic approximation knowledge-distillation framework. Specifically, we employed a thoroughly trained teacher defect detection network equipped with dual semantic approximation information as an experienced teacher to guide the training of a student defect detection network. Experimental results showed that the proposed DSANet-KD achieved better accuracy with a smaller number of parameters than the state-of-the-art methods. To demonstrate the generalizability of DSANet-KD, experiments were conducted on a publicly available RGBD-SOD dataset, whose source code is available at: https://github.com/hjklearn/DSANet-KD.