Cited by 550Advanced Neural Network ApplicationsAdvanced Image Fusion TechniquesAutonomous Vehicle Technology and Safety

The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.

RTFNet: RGB-Thermal Fusion Network for Semantic Segmentation of Urban Scenes

Sun, Yuxiang, Zuo, Weixun, Liu, Ming·2019·IEEE Robotics and Automation Letters

DOI 10.1109/lra.2019.2904733

Open this paper in CoChat — free account

Abstract

Semantic segmentation is a fundamental capability for autonomous vehicles. With the advancements of deep learning technologies, many effective semantic segmentation networks have been proposed in recent years. However, most of them are designed using RGB images from visible cameras. The quality of RGB images is prone to be degraded under unsatisfied lighting conditions, such as darkness and glares of oncoming headlights, which imposes critical challenges for the networks that use only RGB images. Different from visible cameras, thermal imaging cameras generate images using thermal radiations. They are able to see under various lighting conditions. In order to enable robust and accurate semantic segmentation for autonomous vehicles, we take the advantage of thermal images and fuse both the RGB and thermal information in a novel deep neural network. The main innovation of this letter is the architecture of the proposed network. We adopt the encoder–decoder design concept. ResNet is employed for feature extraction and a new decoder is developed to restore the feature map resolution. The experimental results prove that our network outperforms the state of the arts.

Integrity signals

Registry

Crossref · 10.1109/lra.2019.2904733

Cites

31 of 31 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2699184 · Cited by 22,392
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2016) · arXiv · doi:10.48550/arxiv.1606.00915 · Cited by 22,392
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2644615 · Cited by 19,631
Pyramid Scene Parsing Network
Zhao, H., Shi, J., Qi, X., Wang, X., & Jia, J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.660 · Cited by 16,438
Untitled work
(2010) · Cited by 14,134

Show all 30 references

Research works printed but not linked: 1

vollmer, 2017, et�al

Sorted by citations

Items 301–400 of 550

Visible And Infrared Image Fusion Using Encoder-Decoder Network
Ataman, F. C., & Akar, G. B. (2021) · Proceedings - International Conference on Image Processing · doi:10.1109/icip42928.2021.9506740 · Cited by 5
Hierarchical Multimodal Fusion Network with Dynamic Multi-task Learning
Wang, T., & Chen, S. (2021) · IEEE International Conference on Information Reuse and Integration (IRI) · doi:10.1109/iri51335.2021.00034 · Cited by 5
Unveiling the Power of Visible-Thermal Video Object Segmentation
Yang, J., Gao, M., Cong, R., Wang, C., Zheng, F., & Leonardis, A. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3345852 · Cited by 5
Open-Vocabulary RGB-Thermal Semantic Segmentation
Zhao, G., Huang, J., Yan, X., Wang, Z., Tang, J., Ou, Y., Hu, X., & Peng, T. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72904-1_18 · Cited by 6
Fusion of Visible and Infrared Aerial Images from Uncalibrated Sensors Using Wavelet Decomposition and Deep Learning
Vipparla, C., Krock, T. J., Nouduri, K., Fraser, J., Aliakbarpour, H., Sagan, V., Cheng, J.-R. C., & Palaniappan, K. (2024) · Sensors · doi:10.3390/s24248217 · Cited by 5
Towards Dynamic and Small Objects Refinement for Unsupervised Domain Adaptative Nighttime Semantic Segmentation
Pan, J., Li, S., Chen, Y., Zhu, J., & Wang, L. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10801389 · Cited by 5
Improving RGB-Thermal Semantic Scene Understanding With Synthetic Data Augmentation for Autonomous Driving
Li, H., Chu, H. K., & Sun, Y. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3548399 · Cited by 6
Self-citation
First-Person View Hand Segmentation of Multi-Modal Hand Activity Video Dataset
Kim, S., Chi, H., Hu, X., Vegesana, A., & Ramani, K. (2020) · British Machine Vision Conference (BMVC) · doi:10.5244/c.34.140 · Cited by 5
ARTSeg: Employing Attention for Thermal Images Semantic Segmentation
Munir, F., Azam, S., Fatima, U., & Jeon, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-02375-0_27 · Cited by 5
Contrastive fusion representation learning for foreground object detection
Wang, P., Wu, J., Fang, A., Zhu, Z., Wang, C., & Mu, P. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106453 · Cited by 5
Test-Time Adaptation for Nighttime Color-Thermal Semantic Segmentation
Liu, Y., Zhang, W., Zhao, G., Zhu, J., Vasilakos, A. V., & Wang, L. (2023) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2023.3336611 · Cited by 5
S2P2: Self-Supervised Goal-Directed Path Planning Using RGB-D Data for Robotic Wheelchairs
Wang, H., Sun, Y., Fan, R., & Liu, M. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561314 · Cited by 5
Self-citation
An RGB-TIR Dataset from UAV Platform for Robust Urban Traffic Scenes Semantic Segmentation
Ouyang, J., Wang, Q., Shang, Y., Jin, P., Zhong, H., Zhou, L., & Shen, T. (2025) · Scientific Data · doi:10.1038/s41597-025-05994-7 · Cited by 5
Scene adaptation network for visual-thermal urban scene semantic segmentation
Zhang, H., Li, Y., & Chan, L. L.-H. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111166 · Cited by 4
A Multimodal Deep Learning Framework for Accurate Wildfire Segmentation Using RGB and Thermal Imagery
Yue, T., Huang, H., Wang, Q., Song, B., & Chen, Y. (2025) · Applied Sciences · doi:10.3390/app151810268 · Cited by 4
DSDP: Real-Time Asymmetric Dual-Stream Instance Segmentation Embedding Depth-Predictive Architecture for Enhanced Scene Understanding
Chen, M., Li, Q., Nie, W., Liu, J., Geng, J., Ma, Y., & Guan, X. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3613161 · Cited by 4
Deep-learning Grapevine Segmentation in UAV Imagery Across Different Vineyard Environments
Leite, D. R. A., Marques, P., Pádua, L., Sousa, J. J., Morais, R., & Cunha, A. M. T. D. S. (2025) · PFG – Journal of Photogrammetry Remote Sensing and Geoinformation Science · doi:10.1007/s41064-025-00367-6 · Cited by 4
Bridging RGB-T image fusion and semantic segmentation via multi-task collaborative learning
Lu, T., Wu, H., Fu, W., Fang, L., & Shutao Li, A. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103988 · Cited by 4
MMFSeg: Multi-Structure Multi-Feature Fusion for Segmentation of Road Potholes
Feng, Z., Guo, Y., Fan, R., & Sun, Y. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3613629 · Cited by 4
Self-citation
Multimodal Segmentation for Photovoltaic Module Defect Detection
He, X., Tan, J., Hu, T., & Zhu, L. (2025) · IEEE Access · doi:10.1109/access.2025.3640579 · Cited by 4
A Deep Learning-Based Fault Diagnosis Approach for Power System Equipment via Infrared Image Sensing
Liu, H., & Liu, W. (2023) · Journal of Circuits Systems and Computers · doi:10.1142/s0218126624501548 · Cited by 4
Road Anomaly Detection with Unknown Scenes Using DifferNet-Based Automatic Labeling Segmentation
Nguyen, P. T.-T., Nguyen, T.-K., Nguyen, D.-D., Su, S., & Kuo, C. (2024) · Inventions · doi:10.3390/inventions9040069 · Cited by 4
Knowledge-Enhancement Module for RGB-T Semantic Segmentation in Remote Sensing
Wang, Q., Song, H., Ouyang, J., Gu, Y., Song, J., & Shen, T. (2024) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss53475.2024.10642277 · Cited by 4
ERTFNet: Enhanced RGB-T Fusion Network for semantic segmentation by integrating thermal edge features
Yin, H., Zhang, L., Sun, Y., & Yin, G. (2025) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2025.104421 · Cited by 4
Self-citation
Attention-Based Mask R-CNN Enhancement for Infrared Image Target Segmentation
Wang, L., & Ren, K. (2025) · Symmetry · doi:10.3390/sym17071099 · Cited by 4
Cascaded embedded-FPN: A cross-modality multi-scale feature fusion network for varied-sized objects semantic segmentation
Chen, Y., Li, X., Luan, C., Li, C., Ma, J., Zhang, J., Liu, D., Wu, X., & Wei, L. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131081 · Cited by 4
Multimodal SAM-Adapter for Semantic Segmentation
Curti, I., Ramirez, P. Z., Petrelli, A., & Stefano, L. D. (2025) · IEEE Access · doi:10.1109/access.2025.3609640 · Cited by 5
Seeing Heat with Color - RGB-Only Wildfire Temperature Inference from SAM-Guided Multimodal Distillation using Radiometric Ground Truth
Marinaccio, M., & Afghah, F. (2025) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/ieeeconf67917.2025.11443804 · Cited by 4
M-SpecGene: Generalized Foundation Model for RGBT Multispectral Vision
Zhou, K., Yang, F., Wang, S., Wen, B., Zi, C., Chen, L., Shen, Q., & Cao, X. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00737 · Cited by 4
FASFLNet: feature adaptive selection and fusion lightweight network for RGB-D indoor scene parsing
Qian, X., Lin, X., Yu, L., & Zhou, W. (2023) · Optics Express · doi:10.1364/oe.480252 · Cited by 4
Robust Day and Night Object Detection Based on Heterogeneous Sensors and Information Fusion
Yun, Y., Peng, G., Zhang, J., Wen, M., Yang, H., & Wang, D. (2023) · IEEE Conference on Cybernetics and Intelligent Systems · doi:10.1109/cis-ram55796.2023.10370021 · Cited by 4
CIGF-Net: Cross-Modality Interaction and Global-Feature Fusion for RGB-T Semantic Segmentation
Zhiwei, Z., Liu, Y., Xue, W., & Zhuang, Y. (2024) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2024.3462168 · Cited by 4
Mineral segmentation using electron microscope images and spectral sampling through multimodal graph neural networks
Repka, S., Reich, B., Zolotarev, F., Eerola, T., & Zemčík, P. (2025) · Pattern Recognition Letters · doi:10.1016/j.patrec.2025.04.012 · Cited by 4
UHDNet: Unified multimodal fusion harmonization and hierarchical dependency learning for visible-infrared person re-identification
Hong, X., Adam, T., & Ghazali, M. (2025) · Image and Vision Computing · doi:10.1016/j.imavis.2025.105628 · Cited by 4
Lightweight Convolutional Neural Networks with Model-Switching Architecture for Multi-Scenario Road Semantic Segmentation
Lin, P.-W., & Hsu, C.-M. (2021) · Applied Sciences · doi:10.3390/app11167424 · Cited by 4
Semantic Segmentation Algorithm Fusing Infrared and Natural Light Images for Automatic Navigation in Transmission Line Inspection
Yuan, J., Wang, T., Huo, G., Jin, R., & Wang, L. (2023) · Electronics · doi:10.3390/electronics12234810 · Cited by 4
Far-Infrared Object Segmentation Focus on Transmission of Overall Semantic Information
Zang, Y., Yu, B., Yu, L., Yang, D., & Liu, Q. (2020) · IEEE Access · doi:10.1109/access.2020.3028656 · Cited by 4
Nonlinear Spiking Neural Systems for Thermal Image Semantic Segmentation Networks
Wang, P., He, M., Peng, H. J., & Liu, Z. (2025) · International Journal of Neural Systems · doi:10.1142/s0129065725500388 · Cited by 3
CFDHI-Net: Correlation-Driven Feature Decoupling and Hierarchical Integration Network for RGB-Thermal Semantic Segmentation
Chen, H., Wang, Z., Qin, H., & Mu, X. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3581609 · Cited by 3
A weight-sharing based RGB-T image semantic segmentation network with hierarchical feature enhancement and progressive feature fusion
Xue, W., Liu, Y., & Zhuang, Y. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131023 · Cited by 3
CAFNet: Cross-Modal Adaptive Fusion Network With Attention and Gated Weighting for RGB-T Semantic Segmentation
Fu, M., Sun, H., Chen, Z., & Wei, L. (2025) · IEEE Access · doi:10.1109/access.2025.3595811 · Cited by 3
Quilt coverage monitoring via thermal imaging for sleep health
Wang, W., Zhang, X., Zhang, J., Dou, Z., Luo, J., & Zhang, N. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.116313 · Cited by 3
ROD: RGB-Only Fast and Efficient Off-Road Freespace Detection
Sun, T., Ye, H., Mei, J., Chen, L., Zhao, F., Zong, L., & Hu, Y. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11127491 · Cited by 3
DarkSeg: Infrared-Driven Semantic Segmentation for Garment Grasping Detection in Low-Light Conditions
Zhong, H., Tang, F., Chang, H. J., Zhu, X., & Gao, Y. L. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11247198 · Cited by 3
Establishing Reality-Virtuality Interconnections in Urban Digital Twins for Superior Intelligent Road Inspection and Simulation
Zhang, Y., Liu, C.-W., Li, J., Chen, Y., Cheng, J., & Fan, R. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3640982 · Cited by 3
Beyond RGB: Early Stage Fusion of Thermal and Visual Modalities for Robust Maritime Perception
Kafka, O., Rankl, C., & Moser, D. (2025) · Electronics · doi:10.3390/electronics14234746 · Cited by 3
SAM-FuseNet: Segment Anything Guided Multimodal Fusion for RGB–Thermal Aerial Robotic Perception
Zhu, C., Wang, J., Zhang, L., Liang, J., Su, Q., & Li, B. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3648127 · Cited by 3
FTA-Net: integrating Fourier transform and top-k attention for cross-modal feature fusion
Jia, R.-C., Shen, K., Gan, X., & Zhao, D.-L. (2026) · The Journal of Supercomputing · doi:10.1007/s11227-025-08191-w · Cited by 3
E2E-AFNet: An End-to-End adaptive NIR-RGB fusion network applied to solid waste recognition
Ji, T., Liu, H., Liu, J., Liu, C., Jiang, F., Xie, Y., Fang, H., & Yang, J. (2026) · Infrared Physics & Technology · doi:10.1016/j.infrared.2026.106405 · Cited by 3
SamFusion:A model for multimodal image fusion guided by SAM’s rich semantics
Zhang, Y., Ma, Y., & Chai, L. (2026) · Infrared Physics & Technology · doi:10.1016/j.infrared.2026.106438 · Cited by 3
Wavelet-CNet: Wavelet Cross Fusion and Detail Enhancement Network for RGB-Thermal Semantic Segmentation
Zhang, W., Zhang, Q., & Yan, Y. (2026) · Sensors · doi:10.3390/s26031067 · Cited by 3
P3D: Plug-and-play prompt-driven framework for RGB-thermal semantic segmentation
Sun, Y., Dai, C., Wang, H., Wang, L., Li, W. J., Li, M., Zhang, Y., & Yu, A. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.113429 · Cited by 3
Self-citation
Progress in multi-modal image semantic segmentation based on deep learning
Zhao, S., & Zhang, Q. (2023) · Journal of Image and Graphics · doi:10.11834/jig.220451 · Cited by 3
FishSegSSL: A Semi-Supervised Semantic Segmentation Framework for Fish-Eye Images
Paul, S., Patterson, Z., & Bouguila, N. (2024) · Journal of Imaging · doi:10.3390/jimaging10030071 · Cited by 3
Misalignment fusion network for parsing infrared and visible urban scenes
Liu, J., Zhou, W., Zhang, Y., & Luo, T. (2024) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2024.108260 · Cited by 3
Obstacle-sensitive Semantic Bird-Eye-View Map Generation with Boundary-aware Loss for Autonomous driving
Gao, S., Wang, Q., & Sun, Y. (2024) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv55156.2024.10588473 · Cited by 3
Self-citation
MADMFuse: A multi-attribute diffusion model to fuse infrared and visible images
Xu, H., Nie, R., Cao, J., Tan, M., & Ding, Z. (2024) · Digital Signal Processing · doi:10.1016/j.dsp.2024.104741 · Cited by 4
ThermalDiff: A diffusion architecture for thermal image synthesis
Qazi, T., Lall, B., & Mukherjee, P. (2025) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2025.104524 · Cited by 4
Bidirectional Dynamic Adaptation: Mutual Learning with Cross-Network Feature Rectification for Urban Segmentation
Zhang, J., & Chen, N. (2025) · Applied Sciences · doi:10.3390/app151810000 · Cited by 3
Swin-MFA: A Multi-Modal Fusion Attention Network Based on Swin-Transformer for Low-Light Image Human Segmentation
Yi, X., Zhang, H., Wang, Y., Guo, S., Wu, J., & Fan, C. (2022) · Sensors · doi:10.3390/s22166229 · Cited by 3
DaCFN: divide-and-conquer fusion network for RGB-T object detection
Wang, B., Zhao, H., & Zhuang, Y. (2023) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-022-01771-9 · Cited by 3
Quality and content-aware fusion optimization mechanism of infrared and visible images
Weigang, L., Fang, A., Wu, J., & Li, Y. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15237-9 · Cited by 3
Edge-assisted Object Segmentation Using Multimodal Feature Aggregation and Learning
Li, J., Yuan, G., & Yang, Z. (2023) · ACM Transactions on Sensor Networks · doi:10.1145/3612922 · Cited by 3
SPNet: An RGB-D Sequence Progressive Network for Road Semantic Segmentation
Zhou, Z., Zhang, Y., Hua, G., Long, R., Tian, S., & Zou, W. (2023) · IEEE International Workshop on Multimedia Signal Processing (MMSP) · doi:10.1109/mmsp59012.2023.10337660 · Cited by 3
CPKD: Channel and Position-Wise Knowledge Distillation for Segmentation of Road Negative Obstacles
Feng, Z., Guo, Y., & Sun, Y. (2023) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc57777.2023.10422225 · Cited by 3
Self-citation
Exploring Image Fusion Techniques for Off-Road Semantic Segmentation in Harsh Lighting Conditions. A Multispectral Imagery Analysis
Deoli, P., Deshpande, S., Vierling, A., & Berns, K. (2024) · International Conference on Ubiquitous Robots (UR) · doi:10.1109/ur61395.2024.10597528 · Cited by 3
MFFNet: Multimodal feature fusion network for RGB-D transparent object detection
Zhu, L., Li, T., Ning, Y., & Zhang, Y. (2024) · International Journal of Advanced Robotic Systems · doi:10.1177/17298806241283373 · Cited by 3
Thermal image-guided complementary masking with multiscale fusion for multi-spectral image semantic segmentation
Chen, Z., Hu, M., & Chen, B. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110569 · Cited by 3
CLMFNet: Cross-Level Multimodal Fusion Network for RGB-T Semantic Segmentation of Distribution Network Lines
Du, R., Zhang, H., Zhong, H., Huang, Z., & Wang, Y. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3578136 · Cited by 3
Joint self-supervised learning and adversarial adaptation for monocular depth estimation from thermal image
Shin, U., Park, K., Lee, K., Lee, B.-U., & Kweon, I. S. (2023) · Machine Vision and Applications · doi:10.1007/s00138-023-01404-3 · Cited by 3
Lightweight cross-guided contextual perceptive network for visible–infrared urban road scene parsing
Liu, J., Zhou, W., Fang, M., Mao, S., & Yang, R. (2024) · Infrared Physics & Technology · doi:10.1016/j.infrared.2024.105167 · Cited by 3
Enhancing Mirror and Glass Detection in Multimodal Images Based on Mathematical and Physical Methods
Qiu, J., & Jiang, C. (2025) · Mathematics · doi:10.3390/math13050747 · Cited by 2
Generative Model-Based Fusion for Improved Few-Shot Semantic Segmentation of Infrared Images
Yun, J., & Akçakaya, M. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00535 · Cited by 2
RoboFireFuseNet: Robust fusion of visible and infrared wildfire imaging for real-time flame and smoke segmentation
Fotiou, D., Mygdalis, V., & Pitas, I. (2026) · Pattern Recognition Letters · doi:10.1016/j.patrec.2026.04.024 · Cited by 2
SFDFNet: Leveraging spatial-frequency deep fusion for RGB-T semantic segmentation
An, G., Geng, Y., Fang, S., & Guo, J. (2025) · Image and Vision Computing · doi:10.1016/j.imavis.2025.105605 · Cited by 2
A Thermal-Aware Multisensor Framework for Low-Light Image Enhancement
Panda, S. K., & Kumar, P. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3590815 · Cited by 2
LoME: LoRA-Driven Multimodal Extractor for RGB-X Vision Tasks
Zhang, Y., Xie, W., Hui, T., Li, D., Zhang, J., Lei, J., Li, Y., & Fang, L. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3601340 · Cited by 2
Spatial Balancing for RGB-Thermal Semantic Segmentation in Autonomous Driving: A Study From Analysis to Improvement
Li, H., Chu, H. K., & Sun, Y. (2026) · IEEE Transactions on Robotics · doi:10.1109/tro.2026.3677009 · Cited by 2
Self-citation
RTNet: a real-time dual-branch network with cross-attention for urban RGB-T semantic segmentation
Li, C., Zhou, W., Qiao, R., Cai, C., & Zhao, J. (2026) · Journal of Real-Time Image Processing · doi:10.1007/s11554-026-01884-2 · Cited by 2
LiteMFT: Lightweight Multi-Modal Fine-Tuning for Semantic Segmentation
Guo, C., Zhang, Y., Zhang, M., Liu, H., & Li, W. (2026) · IEEE Transactions on Image Processing · doi:10.1109/tip.2026.3702423 · Cited by 2
MUFNet: Toward Semantic Segmentation of Multi-spectral Remote Sensing Images
Xu, F., Shang, Z., Wu, Q., Zhang, X., Lin, Z., & Shao, S. (2021) · Artificial Intelligence and Cloud Computing Conference (AICCC) · doi:10.1145/3508259.3508265 · Cited by 2
DDNet: Depth Dominant Network for Semantic Segmentation of RGB-D Images
Rong, P. (2024) · Sensors · doi:10.3390/s24216914 · Cited by 2
Research on Abnormal Ship Brightness Temperature Detection Based on Infrared Image Edge-Enhanced Segmentation Network
Hong, X., Chen, G., Chen, Y., & Cai, R. (2025) · Applied Sciences · doi:10.3390/app15073551 · Cited by 2
SG-RoadSeg+: End-to-End Freespace Detection Upgraded at Data, Feature, and Loss Levels
Lee, M., Wu, Z., Liu, C., Ye, W., Vityazev, S. V., Chen, Q., & Fan, R. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3579733 · Cited by 2
Depth Enhancement Mask Mapping Network With Multi-Teacher Distillation for RGB-D Scene Parsing
Xiao, Y., Meng, J., Qiang, F., Dong, X., & Zhou, W. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3603265 · Cited by 2
Enhancing Small Object Segmentation in RGB-T Semantic Segmentation for Autonomous Driving
Wan, Y., Shi, Y., Li, Y., & Ren, W. (2025) · International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) · doi:10.1109/aiea66061.2025.11160518 · Cited by 2
Adversarially Robust Multi-Sensor Fusion Model Training Via Random Feature Fusion For Semantic Segmentation
Lee, H. J., & Ro, Y. M. (2021) · Proceedings - International Conference on Image Processing · doi:10.1109/icip42928.2021.9506748 · Cited by 3
Visible-Infrared Features Fusion Based Object Detection
Yang, F., & Cheng, I. (2023) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53992.2023.10394151 · Cited by 2
SIRLUT: Simulated Infrared Fusion Guided Image-adaptive 3D Lookup Tables for Lightweight Image Enhancement
Li, K., Li, H., Li, H., Wang, P., Guo, C., & Jiang, W. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3680918 · Cited by 2
A glance over the past decade: road scene parsing towards safe and comfortable autonomous driving
Fan, R., Li, J., Li, J., Wang, J., Long, Z., Ning, J., Liu, Y., Wang, W., Bocus, M. J., Vityazev, S. V., et al. (2025) · Autonomous Intelligent Systems · doi:10.1007/s43684-025-00096-y · Cited by 2
FCEGNet: Feature calibration and edge-guided MLP decoder Network for RGB-D semantic segmentation
Lu, Y., Ge, B., Xia, C., Zhu, X., Zhang, M., Gao, M., Chen, N., Hu, J., & Zhi, J. (2025) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2025.104448 · Cited by 2
Open-RGBT: Open-Vocabulary RGB-T Zero-Shot Semantic Segmentation in Open-World Environments
Yu, M., Yue, Y., Yang, L., He, X., Yang, Y., & Fu, M. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11127432 · Cited by 2
A Multispectral Automated Transfer Technique (MATT) for machine-driven image labeling utilizing the Segment Anything Model (SAM)Preprint
Gallagher, J., Gogia, A., & Oughton, E. J. (2024) · Research Square · doi:10.21203/rs.3.rs-4518266/v1 · Cited by 2
APFNet: Attention Pyramidal Fusion Network for Semantic Segmentation
Jabu, K. C., Ghorai, M., & Yerra, R. V. P. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-19-7867-8_24 · Cited by 1
Thermal infrared image colorization on nighttime driving scenes with spatially-correlated learning
Wu, L., Shi, Y., Lin, Y., Huang, Z., & Xian, X. (2024) · Journal of Electronic Imaging · doi:10.1117/1.jei.33.2.023019 · Cited by 1
SSA-ECNet:Semantic Segmentation Architecture with Enhanced Cross-Attention Mechanism
Li, M., Xu, Z., Meng, R., Wei, L., & Luo, W. (2024) · International Conference on Advanced Algorithms and Control Engineering (ICAACE) · doi:10.1109/icaace61206.2024.10548981 · Cited by 1
TSTR: A Real-Time RGB-Thermal Semantic Segmentation Model with Multimodal Fusion Transformers
Zhao, G., Yan, X., Cui, A., Hu, C., Bao, J., & Huang, J. (2023) · International Conference on Mobility, Sensing and Networking (MSN) · doi:10.1109/msn60784.2023.00088 · Cited by 1
A Novel Transparent Object Detection Approach Based on Boundary Optimization
Wang, H., Chai, Y., & Tao, T. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-6937-7_27 · Cited by 1
MAITFuse:Multi-Dimension Adaptive Interaction Transform Network For Infrared-visible Image Fusion
Sun, Y., Lei, W., Zhang, Z., Liu, J., Li, C., & Zhang, T. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10889638 · Cited by 1
Self-citation
Mining Scene Structural Guidance for Thermal Images in Self-Supervised Monocular Depth Estimation
Ye, X., Mao, X., Xu, R., & Li, H. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890218 · Cited by 1

Authors: Sun, Y., Zuo, W., & Liu, M.
Affiliations: Hong Kong University of Science and Technology (HK)
ORCID: Sun, Yuxiang
Zuo, Weixun
Liu, Ming
Venue: IEEE Robotics and Automation Letters
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2019-07
Volume: 4
Issue: 3
Pages: 2576-2583
ISSN: 2377-3766 (ISSN-L)
2377-3774
DOI: 10.1109/lra.2019.2904733
OpenAlex: W2921749009
Cited by: 546
References: 30
Citations by year: 2026: 87 · 2025: 127 · 2024: 102 · 2023: 94 · 2022: 51 · 2021: 45 · 2020: 31 · 2019: 9
FWCI: 15.22
Type: journal article
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Robotics and Automation Letters (publishedVersion)
Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
Funders: Shenzhen Science, Technology, and Innovation Commission (JCYJ20160401100022706)
National Natural Science Foundation of China (U1713211)
Hong Kong University of Science and Technology Project (IGN16EG12)
Hong Kong Research Grant Council (11210017, 21202816)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Physical Sciences › Engineering › Media Technology › Advanced Image Fusion Techniques
Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
Keywords: RGB-thermal fusion, semantic segmentation, urban scenes, autonomous vehicles, infrared thermography, encoder-decoder architecture, ResNet feature extraction, low-light conditions, headlight glare

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

RTFNet: RGB-Thermal Fusion Network for Semantic Segmentation of Urban Scenes

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks