Multi-Modal Sensor Fusion-Based Semantic Segmentation for Snow Driving Scenarios
Open accessAbstract
In recent years, autonomous vehicle driving technology and advanced driver assistance systems have played a key role in improving road safety. However, weather conditions such as snow pose severe challenges for autonomous driving and are an active research area. Thanks to their superior reliability, the resilience of detection, and improved accuracy, advances in computation and sensor technology have paved the way for deep learning and neural network–based techniques that can replace the classical approaches. In this research, we investigate the semantic segmentation of roads in snowy environments. We propose a multi-modal fused RGB-T semantic segmentation utilizing a color (RGB) image and thermal map (T) as inputs for the network. This paper introduces a novel fusion module that combines the feature map from both inputs. We evaluate the proposed model on a new snow dataset that we collected and on other publicly available datasets. The segmentation results show that the proposed fused RGB-T input can segregate human subjects in snowy environments better than an RGB-only input. The fusion module plays a vital role in improving the efficiency of multiple input neural networks for person detection. Our results show that the proposed network can generate a higher success rate than other state-of-the-art networks. The combination of our fused module and pyramid supervision path generated the best results in both mean accuracy and mean intersection over union in every dataset.
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MMSFB-snow, using a ResNet50 backbone, achieves an mIoU of 73.8 on the Cityscapes dataset for 2.5D semantic segmentation.
“| MMSFB-snow [88] | ResNet50 [16] | 73.8 |”
From Multimodal semantic segmentation in autonomous driving: A review of current approaches and future perspectives (2022) · cites this paper as [88]
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Cites
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 · see citing works
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
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 · see citing works
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818 · see citing works
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 · see citing works
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 · see citing works
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftPreprint
Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 15,273 · see citing works
The Cityscapes Dataset for Semantic Urban Scene Understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.350 · Cited by 12,433 · see citing works
Fully convolutional networks for semantic segmentation
Shelhamer, E., Long, J., & Darrell, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2572683 · Cited by 11,898 · see citing works
Rethinking Atrous Convolution for Semantic Image Segmentation
Chen, L.-C., Papandreou, G., Schroff, F., & Adam, H. (2017) · arXiv · doi:10.48550/arxiv.1706.05587 · Cited by 7,517 · see citing works
Dual Attention Network for Scene Segmentation
Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., & Lu, H. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00326 · Cited by 7,351 · see citing works
Ros, G., Sellart, L., Materzynska, J., Vazquez, D., & Lopez, A. M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.352 · Cited by 2,338 · see citing works
ICNet for Real-Time Semantic Segmentation on High-Resolution Images
Zhao, H., Qi, X., Shen, X., Shi, J., & Jia, J. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01219-9_25 · Cited by 1,709 · see citing works
Learning Rich Features from RGB-D Images for Object Detection and Segmentation
Gupta, S., Girshick, R., Arbeláez, P., & Malik, J. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10584-0_23 · Cited by 1,528 · see citing works
PSANet: Point-wise Spatial Attention Network for Scene Parsing
Zhao, H., Yi, Z., Liu, S., Shi, J., Loy, C. C., Lin, D., & Jia, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01240-3_17 · Cited by 1,233 · see citing works
FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-Based CNN Architecture
Hazirbas, C., Ma, L., Domokos, C., & Cremers, D. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-54181-5_14 · Cited by 902 · see citing works
MFNet: Towards real-time semantic segmentation for autonomous vehicles with multi-spectral scenes
Ha, Q., Watanabe, K., Karasawa, T., Ushiku, Y., & Harada, T. (2017) · 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8206396 · Cited by 693 · see citing works
Rtfnet: Rgb-thermal fusion network for semantic segmentation of urban scenes
Sun, Y., Zuo, W., & Liu, M. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2904733 · Cited by 546 · see citing works
Indoor scene segmentation using a structured light sensor
Silberman, N., & Fergus, R. (2011) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2011.6130298 · Cited by 485 · see citing works
General Road Detection From a Single Image
Kong, H., Audibert, J.-Y., & Ponce, J. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2045715 · Cited by 417 · see citing works
Path Smoothing Techniques in Robot Navigation: State-of-the-Art, Current and Future Challenges
Ravankar, A. A., Ravankar, A. A., Kobayashi, Y., HOSHINO, Y., & Peng, C.-C. (2018) · Sensors · doi:10.3390/s18093170 · Cited by 320 · see citing works
Self-citationDepth-Aware CNN for RGB-D Segmentation
Wang, W., & Neumann, U. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01252-6_9 · Cited by 314 · see citing works
Self-supervised model adaptation for multimodal semantic segmentation
Valada, A., Mohan, R., & Burgard, W. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01188-y · Cited by 293 · see citing works
Accident risk of road and weather conditions on different road types
Malin, F., Norros, I., & Innamaa, S. (2018) · Accident Analysis & Prevention · doi:10.1016/j.aap.2018.10.014 · Cited by 284 · see citing works
Tian, Z., He, T., Shen, C., & Yan, Y. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00324 · Cited by 270 · see citing works
RedNet: Residual Encoder-Decoder Network for indoor RGB-D Semantic SegmentationPreprint
Jiang, J., Zheng, L., Luo, F., & Zhang, Z. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.01054 · Cited by 233 · see citing works
Effects of Snowfalls on Motor Vehicle Collisions, Injuries, and Fatalities
Eisenberg, D., & Warner, K. E. (2005) · American Journal of Public Health · doi:10.2105/ajph.2004.048926 · Cited by 223 · see citing works
Deep Multispectral Semantic Scene Understanding of Forested Environments Using Multimodal Fusion
Valada, A., Oliveira, G. L., Brox, T., & Burgard, W. (2017) · Springer proceedings in advanced robotics · doi:10.1007/978-3-319-50115-4_41 · Cited by 211 · see citing works
Progressive LiDAR adaptation for road detection
Chen, Z., Zhang, J., & Tao, D. (2019) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2019.1911459 · Cited by 205 · see citing works
A Survey of Semantic SegmentationPreprint
Thoma, M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.06541 · Cited by 175 · see citing works
A History of the Unity Game Engine
Haas, J. K. (2014) · Digital WPI · Cited by 171 · see citing works
Road Surface Status Classification Using Spectral Analysis of NIR Camera Images
Jönsson, P., Casselgren, J., & Thörnberg, B. (2014) · IEEE Sensors Journal · doi:10.1109/jsen.2014.2364854 · Cited by 114 · see citing works
Gao, X., Sun, X., Zhang, Y., Yan, M., Xu, G., Sun, H., Jiao, J., & Fu, K. (2018) · IEEE Access · doi:10.1109/access.2018.2856088 · Cited by 108 · see citing works
An automatic image recognition system for winter road surface condition classification
Omer, R., & Fu, L. (2010) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2010.5625290 · Cited by 107 · see citing works
New System for Detecting Road Ice Formation
Troiano, A., Pasero, E. G. A., & Mesin, L. (2010) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2010.2064910 · Cited by 89 · see citing works
Road condition monitoring system based on a stereo camera
Jokela, M., Kutila, M., & Le, L. (2009) · IEEE International Conference on Intelligent Computer Communication and Processing (ICCP) · doi:10.1109/iccp.2009.5284724 · Cited by 88 · see citing works
A survey on artificial intelligence in Chinese sign language recognition
Jiang, X., Satapathy, S. C., Yang, L., Wang, S.-H., & Zhang, Y.-D. (2020) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-020-04758-2 · Cited by 71 · see citing works
Jiang, X., Hu, B. H., Satapathy, S. C., Wang, S., & Zhang, Y. (2020) · Scientific Programming · doi:10.1155/2020/3291426 · Cited by 69 · see citing works
Traffic Signs Detection and Recognition System using Deep Learning
William, M. M., Zaki, P. S., Soliman, B. K., Alexsan, K. G., Mansour, M., El-Moursy, M. A., & Khalil, K. (2019) · International Conference on Intelligent Computing and Information Systems (ICICIS) · doi:10.1109/icicis46948.2019.9014763 · Cited by 57 · see citing works
Li, Y., Ding, W., Zhang, X., & Ju, Z. (2016) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2016.08.003 · Cited by 45 · see citing works
A real-time road detection method based on reorganized lidar data
Xu, F., Chen, L., Lou, J. J., & Ren, M. (2019) · PLoS ONE · doi:10.1371/journal.pone.0215159 · Cited by 36 · see citing works
Ravankar, A. A., HOSHINO, Y., Ravankar, A. A., Lv, J., Emaru, T., & Kobayashi, Y. (2015) · International Journal of Advanced Robotic Systems · doi:10.5772/59992 · Cited by 35 · see citing works
Self-citationLane Departure detection using image processing techniques
Baili, J., Marzougui, M., Sboui, A., Lahouar, S., Hergli, M., Bose, J. S. C., & Besbes, K. (2017) · International Conference on Anti-Cyber Crimes (ICACC) · doi:10.1109/anti-cybercrime.2017.7905298 · Cited by 27 · see citing works
Semantic Image Segmentation on Snow Driving Scenarios
Lei, Y., Emaru, T., Ravankar, A. A., Kobayashi, Y., & Wang, S. (2020) · IEEE International Conference on Mechatronics and Automation (ICMA) · doi:10.1109/icma49215.2020.9233538 · Cited by 27 · see citing works
Self-citationImpacts of Snowy Weather Conditions on Expressway Traffic Flow Characteristics
Weng, J., Liu, L., & Rong, J. (2013) · Discrete Dynamics in Nature and Society · doi:10.1155/2013/791743 · Cited by 26 · see citing works
John, N. M., Anusha, B., & Kutty, K. K. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.08.002 · Cited by 25 · see citing works
Semantic Segmentation for Road Surface Detection in Snowy Environment
Vachmanus, S., Ravankar, A. A., Emaru, T., & Kobayashi, Y. (2020) · Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE) · doi:10.23919/sice48898.2020.9240402 · Cited by 24 · see citing works
Self-citationTraffic Signs Detection and Recognition System using Deep LearningPreprint
Zaki, P. S., William, M. M., Soliman, B. K., Alexsan, K. G., Khalil, K. K., & El-Moursy, M. A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.03256 · Cited by 22 · see citing works
A hybrid vision-map method for urban road detection
Fernández, C., Fernández-Llorca, D., & Sotelo, M. A. (2017) · Journal of Advanced Transportation · doi:10.1155/2017/7090549 · Cited by 20 · see citing works
Depth-aware CNN for RGB-D SegmentationPreprint
Wang, W., & Neumann, U. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.06791 · Cited by 15 · see citing works
DSNet: An Efficient CNN for Road Scene Segmentation
Chen, P.-R., Hang, H., Chan, S.-W., & Lin, J.-J. (2019) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipaasc47483.2019.9023104 · Cited by 12 · see citing works
A Smart Method to Distinguish Road Surface Conditions at Night-time using a Car-Mounted Camera
Kawai, S., Takeuchi, K., Shibata, K., & Horita, Y. (2014) · IEEJ Transactions on Electronics Information and Systems · doi:10.1541/ieejeiss.134.878 · Cited by 8 · see citing works
An Analysis on Traffic Accidents on Undivided Expressway in Cold and Snow Area
Sano, K., Inagaki, T., Nakano, J., & Nguyen Cao, Y. (2010) · Journal of the Eastern Asia Society for transportation studies/Journal of the Eastern Asia Society for Transportation Studies · doi:10.11175/easts.8.2048 · Cited by 8 · see citing works
Applying Semantic Segmentation to Autonomous Cars in the Snowy EnvironmentPreprint
Pan, Z., Emaru, T., Ravankar, A. A., & Kobayashi, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.12869 · Cited by 8 · see citing works
Self-citationImplementation of an infrared camera for road thermal mapping
Marchetti, M., Moutton, M., Ludwig, S., Ibos, L., Feuillet, V., & Dumoulin, J. (2010) · International Conference on Quantitative InfraRed Thermography (QIRT) · doi:10.21611/qirt.2010.083 · Cited by 7 · see citing works
DSNet: An Efficient CNN for Road Scene SegmentationPreprint
Chen, P.-R., Hang, H., Chan, S.-W., & Lin, J.-J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.05022 · Cited by 6 · see citing works
Road Detection in Snowy Forest Environment using RGB CameraPreprint
Vachmanus, S., Emaru, T., Ravankar, A. A., & Kobayashi, Y. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2212.08511 · Cited by 2 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 2
- G. Somasundaram, "Lane Change Detection and Tracking for a Safe-Lane Approach in Real Time Vision Based Navigation Systems," pp. 345-361, 2011.
- C. Liang, J. Ge, W. Zhang, K. Gui, F. A. Cheikh, and L. Ye, "Winter Road Surface Status Recognition Using Deep Semantic Segmentation Network," in International Workshop on Atmospheric Icing of Structures, (Reykjavík), pp. 1-6, 2019.
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Recent Advances in Thermal Imaging and its Applications Using Machine Learning: A Review
Wilson, A. N., Gupta, K. A., Koduru, B. H., Kumar, A., Jha, A., & Cenkeramaddi, L. R. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3234335 · Cited by 183 · see citing works
Comparative Analysis of Radar and Lidar Technologies for Automotive Applications
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Barua, A., Ahmed, M. U., & Begum, S. (2023) · IEEE Access · doi:10.1109/access.2023.3243854 · Cited by 109 · see citing works
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Hussain, M., O’Nils, M., Lundgren, J., & Mousavirad, S. J. (2024) · IEEE Access · doi:10.1109/access.2024.3508271 · Cited by 107 · see citing works
RGB-T image analysis technology and application: A survey
Song, K., Zhao, Y., Huang, L., Yan, Y., & Meng, Q. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.105919 · Cited by 94 · see citing works
Rizzoli, G., Barbato, F., & Zanuttigh, P. (2022) · Technologies · doi:10.3390/technologies10040090 · Cited by 58 · see citing works
Vachmanus, S., Noraset, T., Piyanonpong, W., Rattananukrom, T., & Tuarob, S. (2023) · IEEE Access · doi:10.1109/access.2023.3345225 · Cited by 53 · see citing works
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Saleem, H., Malekian, R., & Munir, H. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3273913 · Cited by 45 · see citing works
Multi-Modal CNN Features Fusion for Emotion Recognition: A Modified Xception Model
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Positioning of Suspended Permanent Magnet Maglev Trains Using Satellite–Ground Multisensor Fusion
Xu, Y., Fan, K., Hu, Q., & Zhang, X. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3384699 · Cited by 17 · see citing works
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Liu, P., Yu, G., Wang, Z., Zhou, B., Ming, R., & Jin, C. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3266802 · Cited by 14 · see citing works
Zahid, H., Syed, S. A., Rashid, M., Hussain, S., Umer, A., Waheed, A., Nasim, S., Zareei, M., & Mansoor, N. (2023) · Mobile Information Systems · doi:10.1155/2023/1060135 · Cited by 11 · see citing works
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Uhm, T., Park, J., Lee, J., Bae, G.-D., Ki, G., & Choi, Y. (2022) · Electronics · doi:10.3390/electronics11142214 · Cited by 10 · see citing works
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Yi, S., Chen, M., Liu, X., Li, J., & Chen, L. (2023) · Signal Processing Image Communication · doi:10.1016/j.image.2023.117027 · Cited by 10 · see citing works
Kawakubo, S., Arata, S., Demizu, Y., Kamata, T., NARUMI, D., Asawa, T., & Ihara, T. (2023) · Sustainable Cities and Society · doi:10.1016/j.scs.2023.104991 · Cited by 9 · see citing works
Yi, S., Wu, L., Liu, X., Li, J., & Jiang, G. (2023) · Infrared Physics & Technology · doi:10.1016/j.infrared.2023.105077 · Cited by 8 · see citing works
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Rahim, A., Yuan, F., & Barabady, J. (2025) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2025.061575 · Cited by 6 · see citing works
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Huang, S., Kong, X., Yang, Y., Wan, W., & Song, Z. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3527616 · Cited by 6 · see citing works
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He, X., Wu, Y., Zhang, K., Zhu, X., & Yang, X. “. (2023) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/03611981231160544 · Cited by 5 · see citing works
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Fu, Z., Zhang, J., & Tang, B. (2024) · Optics · doi:10.3390/opt5040029 · Cited by 4 · see citing works
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Pan, G., Wang, T., Zhou, X., Du, L., Fu, L., Qiu, J., & Zhang, A. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.130227 · Cited by 1 · see citing works
SCM: Semantic Segmentation with Dual-stream Semantic Synergy under Adverse Weather Conditions
Tian, S., Pang, J., Wang, J., Zhang, B., & Liu, W. (2025) · Multimedia Systems · doi:10.1007/s00530-025-02092-x · Cited by 1 · see citing works
Winter Road Surface Status Detection Method Based on Multimodal Sensing and Hybrid Neural Networks
Hong, X., Hu, R., Gui, K., & Ge, J. (2025) · IEEE International Conference on Electronic Measurement & Instruments · doi:10.1109/icemi66537.2025.11306872 · Cited by 1 · see citing works
Staying Stronger Together: Synergy From Multiple Sensors
Lin, H.-Y. (2022) · Computer · doi:10.1109/mc.2022.3181519 · Cited by 1 · see citing works
Corrections to “Multi-Modal Sensor Fusion-Based Semantic Segmentation for Snow Driving Scenarios”
Vachmanus, S., Ravankar, A. A., Emaru, T., & Kobayashi, Y. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3116408
Self-citationImproving Multimodal Human Pose Estimation by Adversarial Modality Enhancement †
Xia, J., Wu, Q., Guo, Y., Li, Y., Cheng, J., Li, J., & Zhang, Z. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888262
Detectability of pedestrian crossing road markings: comparison of optical and thermal cameras
Pashkevich, A., Bartusiak, J., Grochowski, L., & Peleg, R. (2025) · Transportation research procedia · doi:10.1016/j.trpro.2025.10.063
A Survey on High-Value Target Reasoning for Computer Vision
Tang, X., Zhuang, Z., Xiao, Y., Wang, Y., & Yin, G. (2026) · International Symposium on Intelligent Robotics and Systems (ISoIRS) · doi:10.1109/isoirs70157.2026.11545312
A study on resilience through redundant sensing in autonomous cyber-physical systems
Kortenbusch, P., Majumder, A., Firoud, H., & Kirchner, E. (2026) · Proceedings of the Design Society · doi:10.1017/pds.2026.10637
A Survey on Degraded Image Segmentation
Chen, L., Fu, Y., Shangguan, J., Xu, J., Peng, Y., & Lyu, F. (2026) · Chinese Journal of Electronics · doi:10.23919/cje.2025.00.245
Acoustic Side-Channel Attack on Smartphone via Cross-Sensor Fusion
Afroze, D., Jamarani, A., Tu, Y., & Hei, X. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-6857-3_28
- Authors
- Vachmanus, S., Ravankar, A. A., Emaru, T., & Kobayashi, Y.
- Affiliations
- Hokkaido University (JP)
- Tohoku University (JP)
- National Institute of Technology, Tomakomai College (JP)
- Venue
- IEEE Sensors Journal
- Published
- 2021-08-01
- Volume
- 21
- Issue
- 15
- Pages
- 16839-16851
- ISSN
- 1530-437X (ISSN-L)
- 1558-1748
- 2379-9153
- OpenAlex
- W3157833636
- Cited by
- 53
- References
- 57
- Citations by year
- 2026: 5 · 2025: 15 · 2024: 9 · 2023: 17 · 2022: 6 · 2021: 1
- FWCI
- 2.97
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- IEEE Sensors Journal (publishedVersion)
- Institutional Repositories DataBase (IRDB) (submittedVersion)
- Hokkaido University Collection of Scholarly and Academic Papers (Hokkaido University) (submittedVersion)
- Funders
- Japan Society for the Promotion of Science (JSPS) KAKENHI titled “Robust SLAM by Non-Uniform UGV/UAV Groups for Large Field Management” (JP20K04392)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- semantic segmentation, RGB-thermal fusion, multimodal sensor fusion, autonomous driving, infrared thermography, road segmentation, human detection, fusion module, driver assistance systems
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