AGFNet: Adaptive Gated Fusion Network for RGB-T Semantic Segmentation

作者：Xiaofei Zhou, Xiaoling Wu, Liuxin Bao, Haibing Yin, Qiuping Jiang, Jiyong Zhang · 发表于：IEEE Transactions on Intelligent Transportation Systems · 年份：2025 · DOI：10.1109/tits.2025.3528064 · 被引用次数：37 · 研究领域：Industrial Vision Systems and Defect Detection、Advanced Neural Network Applications

RGB-T semantic segmentation can effectively pop-out objects from challenging scenarios (e.g., low illumination and low contrast environments) by combining RGB and thermal infrared images. However, the existing cutting-edge RGB-T semantic segmentation methods often present insufficient exploration of multi-modal feature fusion, where they overlook the differences between the two modalities. In this paper, we propose an adaptive gated fusion network (AGFNet) to conduct RGB-T semantic segmentation, where the multi-modal features are combined via the gating mechanisms and the spatial details are enhanced via the introduction of edge information. Specifically, the AGFNet employs a cross-modal adaptive gated-attention fusion (CAGF) module to aggregate the RGB and thermal features, where we give a sufficient exploration of the complementarity between the two-modal features via the gated attention unit (GAU). Particularly, in GAU, the gates can be used to purify the features, and the channel and spatial attention mechanisms are further employed to enhance the two-modal features interactively. Then, we design an edge detection (ED) module to learn the object-related edge cues, which simultaneously incorporates local detail information from low-level features and global location information from high-level features. After that, we deploy the edge guidance (EG) module to emphasize the spatial details of the fused features. Next, we deploy the contextual elevation (CE) module to enrich t...