MMSMCNet: Modal Memory Sharing and Morphological Complementary Networks for RGB-T Urban Scene Semantic Segmentation

• Published in: IEEE transactions on circuits and systems for video technology Vol. 33; no. 12; p. 1
• Main Authors: Zhou, Wujie, Zhang, Han, Yan, Weiqing, Lin, Weisi
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: complementary supervision strategy; contour skeleton positioning; Data mining; Decoding; Feature extraction; Image segmentation; information memory sharing; Modules; morphological complementary; Morphology; RGB-T semantic segmentation; Semantic segmentation; Semantics; Skeleton; Thermal imaging
• DOI: 10.1109/TCSVT.2023.3275314
• ISSN: 1051-8215

Combining color (RGB) images with thermal images can facilitate semantic segmentation of poorly lit urban scenes. However, for RGB-thermal (RGB-T) semantic segmentation, most existing models address cross-modal feature fusion by focusing only on exploring the samples while neglecting the connections between different samples. Additionally, although the importance of boundary, binary, and semantic information is considered in the decoding process, the differences and complementarities between different morphological features are usually neglected. In this paper, we propose a novel RGB-T semantic segmentation network, called MMSMCNet, based on modal memory fusion and morphological multiscale assistance to address the aforementioned problems. For this network, in the encoding part, we used SegFormer for feature extraction of bimodal inputs. Next, our modal memory sharing module implements staged learning and memory sharing of sample information across modal multiscales. Furthermore, we constructed a decoding union unit comprising three decoding units in a layer-by-layer progression that can extract two different morphological features according to the information category and realize the complementary utilization of multiscale cross-modal fusion information. Each unit contains a contour positioning module based on detail information, a skeleton positioning module with deep features as the primary input, and a morphological complementary module for mutual reinforcement of the first two types of information and construction of semantic information. Based on this, we constructed a new supervision strategy, that is, a multi-unit-based complementary supervision strategy. Extensive experiments using two standard datasets showed that MMSMCNet outperformed related state-of-the-art methods. The code is available at: https://github.com/2021nihao/MMSMCNet.