Traffic Scene Segmentation Based on RGB-D Image and Deep Learning

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

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works

Very Deep Convolutional Networks for Large-Scale Image Recognition

Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works

ImageNet: A large-scale hierarchical image database

Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · see citing works

Gradient-based learning applied to document recognition

Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805 · see citing works

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 · see citing works

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 · see citing works

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684 · 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

Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741 · see citing works

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 · see citing works

Visualizing and understanding convolutional networks

Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839 · 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

Are we ready for autonomous driving? The KITTI vision benchmark suite

Geiger, A., Lenz, P., & Urtasun, R. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248074 · Cited by 14,897 · 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

Caffe: Convolutional Architecture for Fast Feature Embedding

Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077 · see citing works

Indoor segmentation and support inference from rgbd images

Silberman, N., Hoiem, D., Kohli, P., & Fergus, R. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33715-4_54 · Cited by 5,760 · see citing works

Learning Deconvolution Network for Semantic Segmentation

Noh, H., Hong, S., & Han, B. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.178 · Cited by 4,422 · see citing works

LabelMe: A Database and Web-Based Tool for Image Annotation

Russell, B. C., Torralba, A., Murphy, K. P., & Freeman, W. T. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-007-0090-8 · Cited by 4,210 · see citing works

Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-scale Convolutional Architecture

Eigen, D., & Fergus, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.304 · Cited by 2,862 · 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

Semantic object classes in video: A high-definition ground truth database

Brostow, G., Fauqueur, J., & Cipolla, R. (2008) · Pattern Recognition Letters · doi:10.1016/j.patrec.2008.04.005 · Cited by 1,400 · see citing works

Adaptive deconvolutional networks for mid and high level feature learning

Zeiler, M. D., Taylor, G. W., & Fergus, R. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126474 · Cited by 1,265 · see citing works

Simultaneous Detection and Segmentation

Hariharan, B., Arbeláez, P., Girshick, R., & Malik, J. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10584-0_20 · Cited by 1,176 · see citing works

Feedforward semantic segmentation with zoom-out features

Mostajabi, M., Yadollahpour, P., & Shakhnarovich, G. (2015) · 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.2015.7298959 · Cited by 446 · see citing works

Fast Global Image Smoothing Based on Weighted Least Squares

Min, D., Choi, S., Lu, J., Ham, B., Sohn, K., & Do, M. N. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2366600 · Cited by 368 · see citing works

Active learning for on-road vehicle detection: a comparative study

Sivaraman, S., & Trivedi, M. M. (2011) · Machine Vision and Applications · doi:10.1007/s00138-011-0388-y · Cited by 111 · see citing works

A self-constructing cascade classifier with AdaBoost and SVM for pedestriandetection

Cheng, W.-C., & Jhan, D.-M. (2012) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2012.08.013 · Cited by 36 · see citing works

Algorithm of Vehicle Detection and Pattern Recognition Using SVM

Ge, G. (2007) · Jisuanji gongcheng · Cited by 4 · see citing works

Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things

Zhang, J., & Tao, D. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3039359 · Cited by 774 · see citing works

Real-Time Fusion Network for RGB-D Semantic Segmentation Incorporating Unexpected Obstacle Detection for Road-Driving Images

Sun, L., Yang, K., Hu, X., Hu, W., & Wang, K. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.3007457 · Cited by 174 · see citing works

Real-Time High-Performance Semantic Image Segmentation of Urban Street Scenes

Dong, G., Yan, Y., Shen, C., & Wang, H. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.2980426 · Cited by 148 · see citing works

FEANet: Feature-Enhanced Attention Network for RGB-Thermal Real-time Semantic Segmentation

Deng, F., Feng, H., Liang, M., Wang, H., Yang, Y., Gao, Y., Chen, J., Hu, J., Guo, X., & Lam, T. L. (2021) · 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636084 · Cited by 170 · see citing works

Solving the Security Problem of Intelligent Transportation System With Deep Learning

Lv, Z., Zhang, S., & Xiu, W. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.2980864 · Cited by 144 · see citing works

Applications of Deep Learning in Intelligent Transportation Systems

Haghighat, A., Ravichandra-Mouli, V., Chakraborty, P., Esfandiari, Y., Arabi, S., & Sharma, A. (2020) · Journal of Big Data Analytics in Transportation · doi:10.1007/s42421-020-00020-1 · Cited by 125 · see citing works

Advanced Estimation Techniques for Vehicle System Dynamic State: A Survey

Jin, X., Yin, G., & Chen, N. (2019) · Sensors · doi:10.3390/s19194289 · Cited by 134 · see citing works

Rapid Detection of Blind Roads and Crosswalks by Using a Lightweight Semantic Segmentation Network

Cao, Z., Xu, X., Hu, B., & Zhou, M. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.2989129 · Cited by 81 · see citing works

Meta-seg: A survey of meta-learning for image segmentation

Luo, S., Li, Y., Gao, P., Wang, Y., & Serikawa, S. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108586 · Cited by 79 · see citing works

Soft-Weighted-Average Ensemble Vehicle Detection Method Based on Single-Stage and Two-Stage Deep Learning Models

Wang, H., Yu, Y., Cai, Y., Chen, X., Chen, L., & Li, Y. (2020) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2020.3010832 · Cited by 74 · see citing works

Spatiotemporal Dynamic Forecasting and Analysis of Regional Traffic Flow in Urban Road Networks Using Deep Learning Convolutional Neural Network

Wu, S. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3098461 · Cited by 65 · see citing works

Online estimation ofinertial parameter for lightweight electric vehicle using dual unscented Kalman filter approach

Jin, X., Yang, J., Li, Y., Zhu, B., Wang, J., & Yin, G. (2020) · IET Intelligent Transport Systems · doi:10.1049/iet-its.2019.0458 · Cited by 58 · see citing works

GeoAI: a review of artificial intelligence approaches for the interpretation of complex geomatics data

Pierdicca, R., & Paolanti, M. (2022) · Geoscientific instrumentation, methods and data systems · doi:10.5194/gi-11-195-2022 · Cited by 56 · see citing works

Parallel Complement Network for Real-Time Semantic Segmentation of Road Scenes

Lv, Q., Sun, X., Chen, C., Dong, J., & Zhou, H. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3044672 · Cited by 45 · see citing works

Deep Multi-Branch Aggregation Network for Real-Time Semantic Segmentation in Street Scenes

Weng, X., Yan, Y., Dong, G., Shu, C., Wang, B., Wang, H., & Zhang, J. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3150350 · Cited by 41 · see citing works

Real-Time Semantic Segmentation-Based Stereo Reconstruction

Miclea, V., & Nedevschi, S. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2913883 · Cited by 29 · see citing works

An End-to-End Deep Learning Network for 3D Object Detection From RGB-D Data Based on Hough Voting

Yan, M. G., Li, Z., Yu, X., & Jin, C. (2020) · IEEE Access · doi:10.1109/access.2020.3012695 · Cited by 26 · see citing works

Video Scene Segmentation Using Tensor-Train Faster-RCNN for Multimedia IoT Systems

Dai, C., Liu, X., Yang, L. T., Ni, M., Ma, Z., Zhang, Q., & Deen, M. J. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3022353 · Cited by 26 · see citing works

CARINA Project: Visual Perception Systems Applied for Autonomous Vehicles and Advanced Driver Assistance Systems (ADAS)

Bruno, D. R., Berri, R. A., Barbosa, F. M., & Osório, F. S. (2023) · IEEE Access · doi:10.1109/access.2023.3287491 · Cited by 25 · see citing works

Aptenodytes Forsteri optimization algorithm based on adaptive perturbation of oscillation and mutation operation for image multi-threshold segmentation

Zhang, P., Yang, J., Lou, F., Wang, J., & Sun, X. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120058 · Cited by 21 · see citing works

Depth Embedded Recurrent Predictive Parsing Network for Video Scenes

Zhou, L., Zhang, H., Long, Y., Shao, L., & Yang, J. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2909053 · Cited by 21 · see citing works

Research on 3D Defect Information Management of Drainage Pipeline Based on BIM

Huang, F., Wang, N., Fang, H., Liu, H., & Pang, G. (2022) · Buildings · doi:10.3390/buildings12020228 · Cited by 20 · see citing works

Vehicle-Related Scene Segmentation Using CapsNets

Liu, X., Yan, W. Q., & Kasabov, N. K. (2020) · International Conference on Image and Vision Computing New Zealand (IVCNZ) · doi:10.1109/ivcnz51579.2020.9290664 · Cited by 19 · see citing works

SWNet: A Deep Learning Based Approach for Splashed Water Detection on Road

Qiao, J.-J., Wu, X. Y., He, J.-Y., Li, W., & Peng, Q. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3029006 · Cited by 17 · see citing works

Image semantic segmentation method based on GAN network and ENet model

Hui-yi, L. (2021) · The Journal of Engineering · doi:10.1049/tje2.12067 · Cited by 16 · see citing works

An AI-driven object segmentation and speed control scheme for autonomous moving platforms

Talati, S., Vekaria, D., Kumari, A., & Tanwar, S. (2020) · Computer Networks · doi:10.1016/j.comnet.2020.107783 · Cited by 15 · see citing works

Temporally Consistent Depth Prediction With Flow-Guided Memory Units

Eom, C., Park, H., & Ham, B. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2942096 · Cited by 15 · see citing works

Semantic image segmentation algorithm in a deep learning computer network

He, D., & Xie, C. (2020) · Multimedia Systems · doi:10.1007/s00530-020-00678-1 · Cited by 14 · see citing works

Importance Biased Traffic Scene Segmentation in Diverse Weather Conditions

Liu, Y., Wang, M., Lasang, P., & Sun, Q. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3272922 · Cited by 13 · see citing works

Dense 3D Semantic SLAM of traffic environment based on stereo vision

Li, L., Liu, Z., Ozginer, U., Lian, J., Zhou, Y., & Zhao, Y. (2018) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2018.8500714 · Cited by 12 · see citing works

Low-Rank Sparse Generative Adversarial Unsupervised Domain Adaptation for Multitarget Traffic Scene Semantic Segmentation

Saffari, M., & Khodayar, M. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3291402 · Cited by 11 · see citing works

SABA: Scale-adaptive Attention and Boundary Aware Network for real-time semantic segmentation

Luo, H., Liu, C., & Shark, L. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127680 · Cited by 11 · see citing works

Vision based Vehicle/Pedestrian Detection in Traffic Surveillance System

Suryakala, S., Muthumeenakshi, K., & Gladwin, S. J. (2019) · doi:10.1109/iccsp.2019.8697954 · Cited by 11 · see citing works

G-NET: Accurate Lane Detection Model for Autonomous Vehicle

Wang, H., & Liu, B. (2022) · IEEE Systems Journal · doi:10.1109/jsyst.2022.3210209 · Cited by 10 · see citing works

MSWOA: A Mixed-Strategy-Based Improved Whale Optimization Algorithm for Multilevel Thresholding Image Segmentation

Wang, C., Tu, C., Wei, S., Yan, L., & Wei, F. (2023) · Electronics · doi:10.3390/electronics12122698 · Cited by 9 · see citing works

Polarimetric image augmentation

Blanchon, M., Morel, O., Mériaudeau, F., Seulin, R., & Désiré Sidibé, A. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412133 · Cited by 9 · see citing works

A Novel Hierarchical Model in Ensemble Environment for Road Detection Application

Gu, Y., Si, B., & Liu, B. (2021) · Remote Sensing · doi:10.3390/rs13061213 · Cited by 8 · see citing works

Research on Ship Arrival Law Based on Route Matching and Deep Learning

Pan, N., Ding, Y., Fu, J., Wang, J., & Zheng, H. R. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1952/2/022023 · Cited by 8 · see citing works

3D-DEEP: 3-Dimensional Deep-learning based on elevation patterns for road scene interpretation

Hernández, Á., Woo, S., Corrales, H., Parra, I., Kim, E. H., Llorca, D. F., & Sotelo, M. Á. (2020) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv47402.2020.9304601 · Cited by 7 · see citing works

Keyframes and Shot Boundaries: The Attributes of Scene Segmentation and Classification

Kumar, N., & Sukavanam, N. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-981-13-0761-4_74 · Cited by 7 · see citing works

ColAtt‐Net : In Reducing the Ambiguity of Pedestrian Orientations on Attribute‐Aware Semantic Segmentation Task

Sulistiyo, M. D., Kawanishi, Y., Deguchi, D., Ide, I., Hirayama, T., & Murase, H. (2020) · IEEJ Transactions on Electrical and Electronic Engineering · doi:10.1002/tee.23296 · Cited by 7 · see citing works

Multi-temporal remote sensing image registration based on multi-layer feature fusion of deep residual network

Chen, Y., Guoqing, L., & Chen, H. (2019) · International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) · doi:10.1109/iciibms46890.2019.8991506 · Cited by 5 · see citing works

DiffRSD: Diffusion-Based and Integrity-Aware RGB-D Rail Surface Defect Inspection

Liu, Z., Zhou, J., Huang, R., Fang, X., Tu, Z., & Wang, L. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3617515 · Cited by 4 · see citing works

Semantic Segmentation of Anatomical Structures in Posterior-Anterior Chest X-Ray Image Using U-Net

Kurniawan, B., Sulistiyo, M. D., Hadiyoso, S., & Hashim, N. M. Z. B. (2023) · doi:10.1109/icoict58202.2023.10262815 · Cited by 4 · see citing works

Remote Sensing Image Registration Based on Spatial Transform Network and Phase Correlation Method

Chen, Y., Chen, H., & Guoqing, L. (2019) · International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) · doi:10.1109/iciibms46890.2019.8991540 · Cited by 4 · see citing works

Performance Boost of Attribute-aware Semantic Segmentation via Data Augmentation for Driver Assistance

Sulistiyo, M. D., Kawanishi, Y., Deguchi, D., Ide, I., Hirayama, T., & Murase, H. (2020) · doi:10.1109/icoict49345.2020.9166219 · Cited by 4 · see citing works

Image semantic segmentation method based on GAN network and FCN model

Zhao, L. (2021) · The Journal of Engineering · doi:10.1049/tje2.12085 · Cited by 3 · see citing works

Annotating Covert Hazardous Driving Scenarios Online: Utilizing Drivers' Electroencephalography (EEG) Signals

Chen, Z., Zi, M., Jiang, W., Chu, M., Zhang, Y., Yuan, J., Zhou, G., & Gong, J. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10161448 · Cited by 3 · see citing works

Image semantic segmentation method based on improved ERFNet model

Ye, D., & Han, R. (2021) · The Journal of Engineering · doi:10.1049/tje2.12104 · Cited by 3 · see citing works

MRFDCNet: Multireceptive Field Dense Connection Network for Real-Time Semantic Segmentation

Wang, X., & Cao, W. (2022) · Mobile Information Systems · doi:10.1155/2022/6100292 · Cited by 2 · see citing works

A 703.4-GOPs/W Binary SegNet Processor With Computing-Near-Memory Architecture for Road Detection

Lyu, H., An, F., Zhao, S., Mao, W., & Yu, H. (2020) · IEEE Design and Test · doi:10.1109/mdat.2020.3034041 · Cited by 2 · see citing works

Public Opinion Analysis of Road Accidents & Review of Conventional and Combinational Driver Safety Methodologies with SelfLearning Algorithms

Et. al. Tamilarasi, A. (2021) · Türk bilgisayar ve matematik eğitimi dergisi · doi:10.17762/turcomat.v12i2.2383 · Cited by 2 · see citing works

BSDNet: Balanced Sample Distribution Network for Real-Time Semantic Segmentation of Road Scenes

Ye, L., Zeng, J.-X., Yang, Y., Chimaobi, A. E., & Sekenya, N. M. (2021) · IEEE Access · doi:10.1109/access.2021.3087510 · Cited by 2 · see citing works

Complex Traffic Scene Image Classification Based on Sparse Optimization Boundary Semantics Deep Learning

Xiwei, Z. H. O. U., Wang, H., Saisai, L. I., Peng, H., & Wu, J. (2023) · Wuhan University Journal of Natural Sciences · doi:10.1051/wujns/2023282150 · Cited by 2 · see citing works

Real-time RGB-D Semantic Segmentation With Scale-invariant Depth Encoding and Noise-robust Fusion

Woo, S., Hyun, J., Lee, S., & Kim, E. (2025) · International Journal of Control Automation and Systems · doi:10.1007/s12555-025-0465-z · Cited by 1 · see citing works

UR-SAM: Urban Road Segmentation in Autonomous Driving Scenes Based on SAM

Cheng, J. (2025) · International Conference on Algorithm, Image Processing and Machine Vision (AIPMV) · doi:10.1109/aipmv67185.2025.11290629 · Cited by 1 · see citing works

Semantic Segmentation of Road Traffic Sign based on Improved Deeplabv3+

Ai-ling, D., Wu, J., Song, S., & He, H. (2024) · International Conference on Computer and Communication Systems (ICCCS) · doi:10.1109/icccs61882.2024.10603050 · Cited by 1 · see citing works

Machining surface extraction method for shaft gear parts based on Mask R-CNN

Shi, Y., Yan, P., Su, Y., Wu, D., Guo, Y., Yi, R., & Hu, G. (2021) · International Conference on Electrical Engineering and Mechatronics Technology (ICEEMT) · doi:10.1109/iceemt52412.2021.9602769 · Cited by 1 · see citing works

Reply on RC1

Pierdicca, R. (2022) · Geoscientific instrumentation, methods and data systems · doi:10.5194/gi-2021-32-ac1 · Cited by 1 · see citing works

2018 IndexIEEE Transactions on Intelligent Transportation SystemsVol. 19

(2018) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2018.2884270 · Cited by 1 · see citing works

PREDICTION OF FUEL ANALYSIS USING URBAN BIG DATA

Logeshwari, R. (2018) · International Journal of Advanced Research in Computer Science · doi:10.26483/ijarcs.v9i2.5741

Brain MRI Intelligent Diagnostic Using an Improved Deep Convolutional Neural Network

Zhang, X., Pan, F., & Zhou, L. (2020) · Journal of Medical Imaging and Health Informatics · doi:10.1166/jmihi.2021.3361

Comment on gi-2021-32

Pierdicca, R., & Paolanti, M. (2022) · Geoscientific instrumentation, methods and data systems · doi:10.5194/gi-2021-32-rc1

Comment on gi-2021-32

Pierdicca, R., & Paolanti, M. (2022) · Geoscientific instrumentation, methods and data systems · doi:10.5194/gi-2021-32-rc2

Reply on RC2

Pierdicca, R. (2022) · Geoscientific instrumentation, methods and data systems · doi:10.5194/gi-2021-32-ac2

Enhancement of Target Feature Regions and Intention-Driven Visual Attention Selection in Traffic Scenes

Li, J., Zhang, D., Meng, B., Chen, R., Tang, J., & Wang, Y. (2022) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv51971.2022.9827298

Binary Heatmap based High Speed Object Tracking in Racket Sports using Semantic Image Segmentation

Manikandan, G., Sayf Hussain, Z., & Suryaa, V. S. (2022) · International Journal of Computing and Digital Systems · doi:10.12785/ijcds/1201014

An Active Contour Model Based on Edge and Region Information for Image Segmentation

Zhao, W., & Li, X. (2023) · International Conference on Digital Image Processing · doi:10.1145/3604078.3604101

Transportation safety enhancement by implementing computer vision based models in intelligent transportation systems

Haghighat, A. K. (2022) · doi:10.31274/td-20240329-826

Multilevel thresholding image segmentation using mixed strategy improved convergence based whale optimization algorithm

Naik, J. B., Rajasree, B., Durga, P. P., Sai, V., Sunny, G., & Raghavendra, J. (2025) · i-manager’s Journal on Electronics Engineering · doi:10.26634/jele.15.2.21575

Multilevel thresholding using K-point strategy improved convergence based whale optimization algorithm for image segmentation

Rajesh, B., Palla, L. H., Yalamanchili, V. S., Bandaru, N. K., & Puppala, Y. N. S. K. (2025) · i-manager’s Journal on Image Processing · doi:10.26634/jip.12.1.21733

MLRFNet: Multi-Level Real-Time Fusion Semantic Segmentation Network for Autonomous Driving

Ma, X., Xun, Z., Mao, B., Xun, Y., & Guo, H. (2025) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc61545.2025.10978834

Semantic Segmentation Method of Urban Traffic Images Based on Internet of Things

Zhang, M. (2025) · IEEE Internet of Things Magazine · doi:10.1109/miot.2025.3615257

Obstacle Avoidance in Mobile Robotics: A CNN-Based Approach Using CMYD Fusion of RGB and Depth Images

Mechal, C. E., Mesbah, M., & Idrissi, N. E. A. E. (2026) · Digital · doi:10.3390/digital6010020

Multilevel Thresholdingimage Segmentation Using Mixed Strategy Improved Convergence Based Whaleoptimization Algorithm

Naik, J. B., Rajasree, B., Panchakshari, P. D., Sai, V., Bhargavi, Y. K., & Mallesh, J. R. D. S. (2026) · International Conference on Knowledge Engineering and Communication Systems (ICKECS) · doi:10.1109/ickecs70176.2026.11527965

Semantic segmentation of nighttime road scene based on unsupervised domain adaptation

Che, W., Cai, X., & Qi, J. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.116934

Study on traffic scene semantic segmentation method based on convolutional neural network

Linhui, L. I., Bo, Q. I. A. N., Lian, J., ZHENG, W., & Yafu, Z. H. O. U. (2018) · DOAJ (DOAJ: Directory of Open Access Journals)

Real-time Traffic Scene Segmentation Based on Multi-Feature Map and Deep Learning

Li, L., Zheng, W., Kong, L., Özgüner, Ü., Hou, W., & Lian, J. (2018) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2018.8500467 · Cited by 6 · see citing works

Real-Time Target Detection and Analysis of Complex Traffic Scenes Based on Image Segmentation Algorithm

Tan, X. (2025) · Journal of Combinatorial Mathematics and Combinatorial Computing · doi:10.61091/jcmcc127b-310

Refinement Bilateral Segmentation Network for Semantic Segmentation in Traffic Scenes

Lin, X., & Sun, R. (2022) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2400/1/012014

Depth Augmented Semantic Segmentation Networks for Automated Driving

Rashed, H., Yogamani, S. K., El-Sallab, A., Das, A., & Elhelw, M. (2019) · Communications in computer and information science · doi:10.1007/978-981-15-1387-9_1 · Cited by 5 · see citing works