The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
RTSeg: Real-Time Semantic Segmentation Comparative Study
Open accessAbstract
Semantic segmentation benefits robotics related applications, especially autonomous driving. Most of the research on semantic segmentation only focuses on increasing the accuracy of segmentation models with little attention to computationally efficient solutions. The few work conducted in this direction does not provide principled methods to evaluate the different design choices for segmentation. In this paper, we address this gap by presenting a real-time semantic segmentation benchmarking framework with a decoupled design for feature extraction and decoding methods. The framework is comprised of different network architectures for feature extraction such as VGG16, Resnet18, MobileNet, and ShuffleNet. It is also comprised of multiple meta-architectures for segmentation that define the decoding methodology. These include SkipNet, UNet, and Dilation Frontend. Experimental results are presented on the Cityscapes dataset for urban scenes. The modular design allows novel architectures to emerge, that lead to 143x GFLOPs reduction in comparison to SegNet. This benchmarking framework is publicly available at11https://github.com/MSiam/TFSegmentation.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Bojarski et al. demonstrated end-to-end learning [2] for steering and recently it was applied to fisheye cameras [54].”
From WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving (2019) · cites this paper as [54]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
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
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
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
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
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
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
The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599
Cited by
Sorted by citations
ESPNetv2: A Light-Weight, Power Efficient, and General Purpose Convolutional Neural Network
Mehta, S., Rastegari, M., Shapiro, L., & Hajishirzi, H. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00941 · Cited by 551
WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving
Yogamani, S., Hughes, C., Horgan, J., Sistu, G., Chennupati, S., Uricar, M., Milz, S., Simon, M., Amende, K., Witt, C., et al. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00940 · Cited by 334
“This has been explored in [49, 57, 48] for narrow angle cameras.”
BackgroundSelf-citationEfficient Dense Modules of Asymmetric Convolution for Real-Time Semantic Segmentation
Lo, S.-Y., Hang, H., Chan, S.-W., & Lin, J.-J. (2019) · ACM International Conference on Multimedia in Asia (MMAsia) · doi:10.1145/3338533.3366558 · Cited by 259
DABNet: Depth-wise Asymmetric Bottleneck for Real-time Semantic SegmentationPreprint
Li, G., Yun, I., Kim, J., & Kim, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.11357 · Cited by 264
Polarimetric Convolutional Network for PolSAR Image Classification
Liu, X., Jiao, L., Tang, X., Sun, Q., & Zhang, D. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2879984 · Cited by 134
Semantic scene segmentation in unstructured environment with modified DeepLabV3+
Baheti, B., Innani, S., Gajre, S. S., & Talbar, S. N. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2020.07.029 · Cited by 136
Kamal, U., Tonmoy, T. I., Das, S., & Hasan, M. K. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2911727 · Cited by 126
Deep Neural Networks with Intersection over Union Loss for Binary Image Segmentation
van Beers, F., Lindström, A., Okafor, E., & Wiering, M. (2019) · International Conference on Pattern Recognition Applications and Methods · doi:10.5220/0007347504380445 · Cited by 128
RGB and LiDAR fusion based 3D Semantic Segmentation for Autonomous Driving
El Madawi, K., Rashed, H., El Sallab, A., Nasr, O., Kamel, H., & Yogamani, S. (2019) · 2019 IEEE Intelligent Transportation Systems Conference (ITSC) · doi:10.1109/itsc.2019.8917447 · Cited by 116
Self-citationRashed, H., Ramzy, M., Vaquero, V., Sallab, A. E., Sistu, G., & Yogamani, S. K. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00293 · Cited by 116
Self-citationTavakkoli, A., Kamran, S. A., Hossain, K. F., & Zuckerbrod, S. L. (2020) · Scientific Reports · doi:10.1038/s41598-020-78696-2 · Cited by 106
LiteSeg: A Novel Lightweight ConvNet for Semantic Segmentation
Emara, T., Munim, H. E. A. E., & Abbas, H. M. (2019) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta47822.2019.8945975 · Cited by 92
ADMNet: Attention-Guided Densely Multi-Scale Network for Lightweight Salient Object Detection
Zhou, X., Shen, K., & Liu, Z. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3413529 · Cited by 88
Bayesian Spatial Kernel Smoothing for Scalable Dense Semantic Mapping
Gan, L., Zhang, R., Grizzle, J. W., Eustice, R. M., & Ghaffari, M. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.2965390 · Cited by 89
Deep learning-based precision agriculture through weed recognition in sugar beet fields
Nasiri, A., Omid, M., Taheri‐Garavand, A., & Jafari, A. (2022) · Sustainable Computing Informatics and Systems · doi:10.1016/j.suscom.2022.100759 · Cited by 83
Let’s Get Dirty: GAN Based Data Augmentation for Camera Lens Soiling Detection in Autonomous Driving
Uricar, M., Sistu, G., Rashed, H., Vobecky, A., Kumar, V. R., Krizek, P., Burger, F., & Yogamani, S. (2021) · 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv48630.2021.00081 · Cited by 66
Self-citationHR-NAS: Searching Efficient High-Resolution Neural Architectures with Lightweight Transformers
Ding, M., Lian, X., Yang, L., Wang, P., Jin, X., Lu, Z., & Luo, P. (2021) · 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/cvpr46437.2021.00300 · Cited by 67
Leveraging Stereo-Camera Data for Real-Time Dynamic Obstacle Detection and Tracking
Eppenberger, T., Cesari, G., Dymczyk, M., Siegwart, R., & Dubé, R. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9340699 · Cited by 66
NDNet: Narrow While Deep Network for Real-Time Semantic Segmentation
Yang, Z., Yu, H., Fu, Q., Sun, W. J., Jia, W., Sun, M., & Mao, Z. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.2987816 · Cited by 58
DFFNet: An IoT-perceptive dual feature fusion network for general real-time semantic segmentation
Tang, X., Tu, W., Li, K., & Cheng, J. (2021) · Information Sciences · doi:10.1016/j.ins.2021.02.004 · Cited by 57
Li, G., Jiang, S., Yun, I., Kim, J., & Kim, J. (2020) · IEEE Access · doi:10.1109/access.2020.2971760 · Cited by 53
Yang, Q., Liu, M., Zhang, Z., Yang, S., Ning, J., & Han, W. (2019) · Remote Sensing · doi:10.3390/rs11172008 · Cited by 55
Near-Field Perception for Low-Speed Vehicle Automation Using Surround-View Fisheye Cameras
Eising, C., Horgan, J., & Yogamani, S. K. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3127646 · Cited by 58
Self-citationDiCENet: Dimension-Wise Convolutions for Efficient Networks
Mehta, S., Hajishirzi, H., & Rastegari, M. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3041871 · Cited by 56
An Accurate Nuclei Segmentation Algorithm in Pathological Image Based on Deep Semantic Network
Pan, X., Li, L., Yang, D., He, Y., Liu, Z., & Yang, H. (2019) · IEEE Access · doi:10.1109/access.2019.2934486 · Cited by 49
Multi-Scale Depthwise Separable Convolution for Semantic Segmentation in Street–Road Scenes
Dai, Y., Li, C., Su, X., Liu, H., & Li, J. (2023) · Remote Sensing · doi:10.3390/rs15102649 · Cited by 47
FisheyeMODNet: Moving Object Detection on Surround-view Cameras for Autonomous Driving
Yahiaoui, M. (2019) · TU Dublin · doi:10.21427/v1ar-t994 · Cited by 41
Self-citationCostanzo, S., Flores, A. M., & Buonanno, G. (2023) · IEEE Access · doi:10.1109/access.2023.3291076 · Cited by 40
Overview and Empirical Analysis of ISP Parameter Tuning for Visual Perception in Autonomous Driving
Yahiaoui, L., Horgan, J., Deegan, B. M., Yogamani, S. K., Eising, C., & Denny, P. E. (2019) · Journal of Imaging · doi:10.3390/jimaging5100078 · Cited by 36
Self-citationDeep learning based torsional nystagmus detection for dizziness and vertigo diagnosis
Zhang, W., Wu, H., Liu, Y., Zheng, S., Liu, Z., Li, Y., Zhao, Y., & Zhu, Z. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102616 · Cited by 37
Dai, Y., Zheng, T., Xue, C., & Zhou, L. (2022) · Remote Sensing · doi:10.3390/rs14246297 · Cited by 35
C3: Concentrated-Comprehensive Convolution and its application to semantic segmentationPreprint
Park, H., Yoo, Y., Seo, G., Han, D., Yun, S., & Kwak, N. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.04920 · Cited by 50
Proof of Concept for Sea Ice Stage of Development Classification Using Deep Learning
Kruk, R., Fuller, M. C., Komarov, A. S., Isleifson, D., & Jeffrey, I. (2020) · Remote Sensing · doi:10.3390/rs12152486 · Cited by 34
MDRNet: a lightweight network for real-time semantic segmentation in street scenes
Dai, Y., Wang, J., Li, J., & Li, J. (2021) · Assembly Automation · doi:10.1108/aa-06-2021-0078 · Cited by 32
Low-Power Neural Networks for Semantic Segmentation of Satellite Images
Bahl, G., Daniel, L., Moretti, M., & Lafarge, F. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00302 · Cited by 36
Mohamed, E., Ewaisha, M., Siam, M., Rashed, H., Yogamani, S. K., Hamdy, W., El-Dakdouky, M., & El-Sallab, A. (2021) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv48863.2021.9575445 · Cited by 31
Self-citationMIFNet: A lightweight multiscale information fusion network
Cheng, J., Peng, X., Tang, X., Tu, W., & Xu, W. (2021) · International Journal of Intelligent Systems · doi:10.1002/int.22804 · Cited by 28
Dahal, A., Hossen, M. J., Chennupati, S., Sistu, G., Malhan, K., Amasha, M., & Yogamani, S. K. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00287 · Cited by 28
Self-citationeFishery, M. T. N., Rachman, F. D., Akbar, M., eFishery, M. T. N., & Putera, E. (2023) · international Conference on Fisheries and Aquaculture · doi:10.17501/23861282.2023.8102 · Cited by 26
Efficient Dense Modules of Asymmetric Convolution for Real-Time Semantic SegmentationPreprint
Lo, S.-Y., Hang, H., Chan, S.-W., & Lin, J.-J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.06323 · Cited by 26
Boundary-Aware CNN for Semantic Segmentation
Zou, N., Xiang, Z., Chen, Y., Chen, S., & Qiao, C. (2019) · IEEE Access · doi:10.1109/access.2019.2935816 · Cited by 26
Efficient Dual-Branch Bottleneck Networks of Semantic Segmentation Based on CCD Camera
Li, J., Dai, Y., Su, X., & Wu, W. (2022) · Remote Sensing · doi:10.3390/rs14163925 · Cited by 25
Multi-Classes and Motion Properties for Concurrent Visual SLAM in Dynamic Environments
Yang, B., Ran, W., Wang, L., Lu, H., & Chen, Y. P. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3110667 · Cited by 24
RGB and LiDAR fusion based 3D Semantic Segmentation for Autonomous DrivingPreprint
Madawy, K. E., Rashed, H., Sallab, A. E., Nasr, O. A., Kamel, H., & Yogamani, S. K. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.00208 · Cited by 23
Self-citationReal-time semantic segmentation with weighted factorized-depthwise convolution
Hao, X., Hao, X., Zhang, Y., Li, Y., & Wu, C. (2021) · Image and Vision Computing · doi:10.1016/j.imavis.2021.104269 · Cited by 22
Amosov, O. S., Amosova, S. G., Ivanov, Y. S., & Жиганов, С. В. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05170-5 · Cited by 22
Semantic Segmentation with Peripheral Vision
Mozaffari, M. H., & Lee, W. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-64559-5_33 · Cited by 21
Context-Integrated and Feature-Refined Network for Lightweight Object Parsing
Jiang, B., Tu, W., Yang, C., & Yuan, J. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2978583 · Cited by 21
Hespeler, S. C., Dehghan-Niri, E., Juhasz, M., Luo, K., & Halliday, H. S. (2022) · Applied Sciences · doi:10.3390/app12188974 · Cited by 21
Human Related-Health Actions Detection using Android Camera based on TensorFlow Object Detection API
Al-Azzoa, F., Taqi, A. M., & Milanova, M. G. (2018) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2018.091002 · Cited by 20
Towards practical 2D grapevine bud detection with fully convolutional networks
Marset, W. V., Pérez, D., Díaz, C. A., & Bromberg, F. (2021) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105947 · Cited by 19
EFRNet: Efficient Feature Reuse Network for Real-time Semantic Segmentation
Li, Y., Li, M., Li, Z., Xiao, C., & Li, H. (2022) · Neural Processing Letters · doi:10.1007/s11063-022-10740-w · Cited by 17
Print Defect Mapping with Semantic Segmentation
Valente, A. C., Wada, C., Neves, D., Neves, D., Perez, F. V. M., Megeto, G. A. S., Cascone, M. H., Gomes, O., & Lin, Q. (2020) · 2020 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv45572.2020.9093470 · Cited by 16
“onvolutional DCNNs, such as FCN [11], U-Net [14], and DeepLab [4]. Applications of semantic segmentation are diverse, including medical image analysis [14], remote sensing [22] and autonomous driving [15]. In this work, we also employ semantic segmentation techniques, specifically DeepLab-v3+, to the problem of print d…”
MethodEnhancing V-SLAM Keyframe Selection with an Efficient ConvNet for Semantic Analysis
Alonso, Í., Riazuelo, L., & Murillo, A. C. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793923 · Cited by 16
RST-MODNet: Real-time Spatio-temporal Moving Object Detection for Autonomous DrivingPreprint
Ramzy, M., Rashed, H., Sallab, A. E., & Yogamani, S. K. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.00438 · Cited by 16
Self-citationReal-Time Segmentation with Appearance, Motion and Geometry
Siam, M., Eikerdawy, S., Gamal, M., Abdel-Razek, M., Jägersand, M., & Zhang, H. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8594088 · Cited by 15
Self-citationMarchesi, G., Eichhorn, C., Plecher, D. A., Itoh, Y., & Klinker, G. (2021) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi10110772 · Cited by 15
Semantic Segmentation of Vineyard Images Using Convolutional Neural Networks
Kalampokas, Τ., Tziridis, K., Nikolaou, A., Vrochidou, Ε., Papakostas, G. A., Pachidis, T. P., & Kaburlasos, V. G. (2020) · Proceedings of the international neural networks society · doi:10.1007/978-3-030-48791-1_22 · Cited by 15
A lightweight network with attention decoder for real-time semantic segmentation
Wang, K., Yang, J., Yuan, S., & Li, M. (2021) · The Visual Computer · doi:10.1007/s00371-021-02115-4 · Cited by 14
Weed Density Estimation Using Semantic Segmentation
Asad, M., & Bais, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-39770-8_13 · Cited by 14
Trained Trajectory based Automated Parking System using Visual SLAM.Preprint
Tripathi, N., Sistu, G., & Yogamani, S. K. (2020) · arXiv (Cornell University) · Cited by 14
Self-citationRaza, S. M., Abidi, S. M. H., Masuduzzaman, M., & Shin, S. Y. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131357 · Cited by 13
Deep learning in acute vertigo diagnosis
Rastall, D. P. W., & Green, K. E. (2022) · Journal of the Neurological Sciences · doi:10.1016/j.jns.2022.120454 · Cited by 13
LDPNet: A Lightweight Densely Connected Pyramid Network for Real-Time Semantic Segmentation
Hu, X., & Jing, L. (2020) · IEEE Access · doi:10.1109/access.2020.3038864 · Cited by 13
Semantic Segmentation of Maxillary Teeth and Palatal Rugae in Two-Dimensional Images
Bsat, A. R. E., Shammas, E., Asmar, D., Sakr, G. E., Zeno, K. G., Macari, A. T., & Ghafari, J. G. (2022) · Diagnostics · doi:10.3390/diagnostics12092176 · Cited by 12
Rapson, C., Seet, B., Naeem, M. A., Lee, J. E., & Klette, R. (2019) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2019.8917087 · Cited by 10
Efficient Palm-Line Segmentation with U-Net Context Fusion Module
Văn, T. P., Nguyen, S. T., Doan, L. B., Tran, N., & Thanh, T. M. (2020) · International Conference on Advanced Computing and Applications (ACOMP) · doi:10.1109/acomp50827.2020.00011 · Cited by 11
Tian, K., Zheng, Y., Sun, Z., Yin, Z., Zhu, K., & Liu, C. (2025) · Sensors · doi:10.3390/s25041168 · Cited by 9
Strategies for large scale elastic and semantic LiDAR reconstruction
Wang, Y., Ramezani, M., Mattamala, M., Digumarti, S. T., & Fallon, M. F. (2022) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2022.104185 · Cited by 10
DEEP SEMANTIC SEGMENTATION FOR THE OFF-ROAD AUTONOMOUS DRIVING
Sgibnev, I. V., Sorokin, A., Vishnyakov, B. V., & Vizilter, Y. V. (2020) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xliii-b2-2020-617-2020 · Cited by 9
Learning Panoptic Segmentation from Instance Contours
Chennupati, S., Narayanan, V., Sistu, G., Yogamani, S. K., & Rawashdeh, S. A. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9560798 · Cited by 9
Self-citationThe Segmentation of Road Scenes Based on Improved ESPNet Model
Jin, R., Yu, T., Han, X., & Liu, Y. (2021) · Security and Communication Networks · doi:10.1155/2021/1681952 · Cited by 8
RetractedMTNAS: Search Multi-task Networks for Autonomous Driving
Liu, H., Dong, L., Peng, J., Zhao, Q., Tian, L., & Shan, Y. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-69535-4_41 · Cited by 7
Traditional Method Meets Deep Learning in an Adaptive Lane and Obstacle Detection System
Luu, V.-T., Huynh, V.-C., Tran, V.-H., Nguyen, T.-H., & Phu, T.-N.-H. (2020) · International Conference on Green Technology and Sustainable Development (GTSD) · doi:10.1109/gtsd50082.2020.9303108 · Cited by 7
ADFNet: accumulated decoder features for real‐time semantic segmentation
Choi, H., Ahn, H., Kim, J., & Jeon, M. (2020) · IET Computer Vision · doi:10.1049/iet-cvi.2019.0289 · Cited by 7
Trained Trajectory based Automated Parking System using Visual SLAM on Surround View CamerasPreprint
Tripathi, N., & Yogamani, S. K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.02161 · Cited by 7
Self-citationA Lightweight High Definition Mapping Method Based on Multi-Source Data Fusion Perception
Song, H., Hu, B., Huang, Q., Zhang, Y., & Song, J. (2023) · Applied Sciences · doi:10.3390/app13053264 · Cited by 6
RRNet: Repetition-Reduction Network for Energy Efficient Depth Estimation
Oh, S., Kim, H.-J. S., Lee, J., & Kim, J. (2020) · IEEE Access · doi:10.1109/access.2020.3000773 · Cited by 6
SEMANTIC SCENE UNDERSTANDING FOR THE AUTONOMOUS PLATFORM
Vishnyakov, B. V., Blokhinov, Y. B., Sgibnev, I. V., Sheverdin, V., Sorokin, A., Nikanorov, A., Masalov, P., Kazakhmedov, K., Brianskiy, S. A., Andrienko, E. E., et al. (2020) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xliii-b2-2020-637-2020 · Cited by 6
Yang, M., & Shi, Y. (2019) · Communications in computer and information science · doi:10.1007/978-3-030-36808-1_29 · Cited by 5
Kuang, H., Li, Y., Zhang, Y., Wan, Y., & Ge, G. (2022) · Robotica · doi:10.1017/s026357472200073x · Cited by 5
Self-supervised Sparse to Dense Motion Segmentation
Kardoost, A., Ho, K., Ochs, P., & Keuper, M. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-69532-3_26 · Cited by 6
On the Structures of Representation for the Robustness of Semantic Segmentation to Input Corruption
Lehman, C., Temel, D., & AlRegib, G. (2020) · Proceedings - International Conference on Image Processing · doi:10.1109/icip40778.2020.9190912 · Cited by 5
Visual Quality Inspection of Pomegranate Crop Using a Novel Dataset and Deep Learning
Koufatzis, A., Vrochidou, Ε., & Papakostas, G. A. (2022) · International Conference on Systems, Signals and Image Processing · doi:10.1109/iwssip55020.2022.9854435 · Cited by 4
DriveSpace: Towards context-aware drivable area detection
Eising, C., Chandra, S., Sistu, G., Horgan, J., Deegan, B. M., Chennupati, S., & Yogamani, S. K. (2019) · Electronic Imaging · doi:10.2352/issn.2470-1173.2019.15.avm-042 · Cited by 4
Self-citationA Machine Learning Based Approach to Crack Detection in Asphalt Pavements
Balaji, A. J., Balaji, G. T., Dinesh, M. S., Nair, B. B., & Ram, D. S. H. (2018) · IEEE India Conference · doi:10.1109/indicon45594.2018.8987039 · Cited by 4
GEO-TAGGED IMAGE RETRIEVAL FROM MAPILLARY STREET IMAGES FOR A TARGET BUILDING
Celik, N., & Sümer, E. (2020) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xliv-4-w3-2020-151-2020 · Cited by 4
Design of Steel Surface Defect Detection System Based on deep Learning
Liu, M., Huang, Y., & Xie, Y. (2022) · IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) · doi:10.1109/icaica54878.2022.9844624 · Cited by 4
4D Temporally Coherent Multi-Person Semantic Reconstruction and Segmentation
Mustafa, A., Russell, C., & Hilton, A. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01599-4 · Cited by 4
Hybrid Dilated Convolution Network Using Attentive Kernels for Real-Time Semantic Segmentation
He, J., Jiang, B., Yang, C., & Tu, W. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60633-6_11 · Cited by 4
Leveraging Pretrained Image Classifiers for Language-Based Segmentation
Golub, D., El-Kishky, A., & Martín-Martín, R. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093453 · Cited by 4
Eising, C., Horgan, J., & Yogamani, S. K. (2021) · arXiv (Cornell University) · Cited by 4
Self-citationSong, X., Liu, H., Wang, L., Wang, S., Cao, Y., Xu, D., & Zhang, S. (2022) · Traitement du signal · doi:10.18280/ts.390416 · Cited by 3
Environmental Perception Using Fish-Eye Cameras for Autonomous Driving
Qian, Y., Yang, M. Z., & Dolan, J. M. (2023) · Advances in computer vision and pattern recognition · doi:10.1007/978-981-99-4287-9_2 · Cited by 3
A mask area based grasping force control strategy for force sensor-less robot
Zhang, S., Li, S., Fu, Y. H., Xiong, Y., & Xie, Z. Y. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12016-w · Cited by 3
RTSNet: Real-Time Semantic Segmentation Network For Outdoor Scenes
Ma, M., Zou, F., Xu, F., & Song, J. (2019) · IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems · doi:10.1109/cyber46603.2019.9066620 · Cited by 3
Meng, P., Jia, S.-C., & Li, Q. (2023) · Complex & Intelligent Systems · doi:10.1007/s40747-023-01046-y · Cited by 2
Yang, Z., Liang, K., Shi, Z., Han, Q., Zhao, W., & Zeng, Z. (2024) · IEEE International Conference on Image Processing and Computer Applications (ICIPCA) · doi:10.1109/icipca61593.2024.10709193 · Cited by 2
RRNet: Repetition-Reduction Network for Energy Efficient Decoder of Depth EstimationPreprint
Oh, S., Kim, H.-J. S., Lee, J., & Kim, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.09707 · Cited by 2
Semantic Segmentation of Land Cover in Multisource Aerial Imagery Using U-Net
Mulachela, S. R., Setiawan, E. B., & Kosala, G. (2025) · International Conference on Advancement in Data Science, E-learning and Information System (ICADEIS) · doi:10.1109/icadeis65852.2025.10933466 · Cited by 1
Metadata
- Authors
- Siam, M., Gamal, M., Abdel-Razek, M., Yogamani, S., & Jagersand, M.
- Affiliations
- Cairo University (EG)
- Venue
- 2018 25th IEEE International Conference on Image Processing (ICIP)
- Publisher
- IEEE
- Published
- 2018-10
- Pages
- 1603-1607
- ISSN
- 1522-4880 (ISSN-L)
- OpenAlex
- W2790381683
- Cited by
- 112
- References
- 21
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 2 · 2023: 7 · 2022: 16 · 2021: 20 · 2020: 27 · 2019: 25 · 2018: 6
- FWCI
- 5.88
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Proceedings - International Conference on Image Processing (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- TIB Data Manager
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Keywords
- real-time semantic segmentation, benchmarking framework, computational efficiency, feature extraction, decoding algorithms, meta-architecture, cityscape, autonomous driving, robotics, modular network design
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
