{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T01:08:51Z","timestamp":1780708131500,"version":"3.54.1"},"reference-count":52,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Digital Signal Processing"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.dsp.2026.106278","type":"journal-article","created":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T06:53:39Z","timestamp":1779346419000},"page":"106278","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["TSNet: Three-branch scale-aware network for real-time semantic segmentation"],"prefix":"10.1016","volume":"181","author":[{"given":"Junxing","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1389-9089","authenticated-orcid":false,"given":"Wenbin","family":"Zou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Su","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"2","key":"10.1016\/j.dsp.2026.106278_bib0001","doi-asserted-by":"crossref","first-page":"1586","DOI":"10.1109\/TITS.2023.3313982","article-title":"SegTransConv: transformer and CNN hybrid method for real-time semantic segmentation of autonomous vehicles","volume":"25","author":"Fan","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"8","key":"10.1016\/j.dsp.2026.106278_bib0002","doi-asserted-by":"crossref","first-page":"7440","DOI":"10.1109\/TCSVT.2024.3370685","article-title":"Uncertainty-aware hierarchical aggregation network for medical image segmentation","volume":"34","author":"Zhou","year":"2024","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"8","key":"10.1016\/j.dsp.2026.106278_bib0003","doi-asserted-by":"crossref","first-page":"4764","DOI":"10.1109\/TSMC.2023.3257416","article-title":"Convolutional neural network-based robot control for an eye-in-hand camera","volume":"53","author":"Guo","year":"2023","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"issue":"3","key":"10.1016\/j.dsp.2026.106278_bib0004","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.1109\/TITS.2020.2972974","article-title":"Deep multi-modal object detection and semantic segmentation for autonomous driving: datasets, methods, and challenges","volume":"22","author":"Feng","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.dsp.2026.106278_bib0005","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"3431","article-title":"Fully convolutional networks for semantic segmentation","author":"Long","year":"2015"},{"key":"10.1016\/j.dsp.2026.106278_bib0006","series-title":"Medical Image Computing and Computer-assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5\u20139, 2015, Proceedings, Part III 18","first-page":"234","article-title":"U-Net: convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"issue":"4","key":"10.1016\/j.dsp.2026.106278_bib0007","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.dsp.2026.106278_bib0008","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"2881","article-title":"Pyramid scene parsing network","author":"Zhao","year":"2017"},{"key":"10.1016\/j.dsp.2026.106278_bib0009","series-title":"Proceedings of the European Conference on Computer Vision (ECCV)","first-page":"405","article-title":"Icnet for real-time semantic segmentation on high-resolution images","author":"Zhao","year":"2018"},{"issue":"1","key":"10.1016\/j.dsp.2026.106278_bib0010","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1109\/TCSVT.2016.2600261","article-title":"Computation and memory efficient image segmentation","volume":"28","author":"Zhou","year":"2016","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.dsp.2026.106278_bib0011","unstructured":"D. Mazzini, Guided upsampling network for real-time semantic segmentation, (2018). arXiv preprint:1807.07466."},{"key":"10.1016\/j.dsp.2026.106278_bib0012","unstructured":"A. Paszke, A. Chaurasia, S. Kim, E. Culurciello, ENet: a deep neural network architecture for real-time semantic segmentation, (2016). arXiv preprint:1606.02147."},{"key":"10.1016\/j.dsp.2026.106278_bib0013","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"1251","article-title":"Xception: deep learning with depthwise separable convolutions","author":"Chollet","year":"2017"},{"issue":"12","key":"10.1016\/j.dsp.2026.106278_bib0014","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: a deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.dsp.2026.106278_bib0015","series-title":"Proceedings of the European Conference on Computer Vision (ECCV)","first-page":"325","article-title":"BiSeNet: bilateral segmentation network for real-time semantic segmentation","author":"Yu","year":"2018"},{"issue":"3","key":"10.1016\/j.dsp.2026.106278_bib0016","doi-asserted-by":"crossref","first-page":"3448","DOI":"10.1109\/TITS.2022.3228042","article-title":"Deep dual-resolution networks for real-time and accurate semantic segmentation of traffic scenes","volume":"24","author":"Pan","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.dsp.2026.106278_bib0017","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"issue":"4","key":"10.1016\/j.dsp.2026.106278_bib0018","doi-asserted-by":"crossref","first-page":"3679","DOI":"10.1109\/TCSVT.2024.3509504","article-title":"DSNet: a novel way to use atrous convolutions in semantic segmentation","volume":"35","author":"Guo","year":"2024","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.dsp.2026.106278_bib0019","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"19529","article-title":"PIDNet: a real-time semantic segmentation network inspired by PID controllers","author":"Xu","year":"2023"},{"key":"10.1016\/j.dsp.2026.106278_bib0020","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"3213","article-title":"The cityscapes dataset for semantic urban scene understanding","author":"Cordts","year":"2016"},{"issue":"2","key":"10.1016\/j.dsp.2026.106278_bib0021","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.patrec.2008.04.005","article-title":"Semantic object classes in video: a high-definition ground truth database","volume":"30","author":"Brostow","year":"2009","journal-title":"Pattern Recognit. Lett."},{"key":"10.1016\/j.dsp.2026.106278_bib0022","series-title":"Proceedings of the European Conference on Computer Vision (ECCV)","first-page":"801","article-title":"Encoder-decoder with atrous separable convolution for semantic image segmentation","author":"Chen","year":"2018"},{"key":"10.1016\/j.dsp.2026.106278_bib0023","unstructured":"L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, A.L. Yuille, Semantic image segmentation with deep convolutional nets and fully connected CRFs, (2014). arXiv preprint:1412.7062."},{"key":"10.1016\/j.dsp.2026.106278_bib0024","unstructured":"L.-C. Chen, G. Papandreou, F. Schroff, H. Adam, Rethinking atrous convolution for semantic image segmentation, (2017). arXiv preprint:1706.05587."},{"key":"10.1016\/j.dsp.2026.106278_bib0025","unstructured":"K. Sun, Y. Zhao, B. Jiang, T. Cheng, B. Xiao, D. Liu, Y. Mu, X. Wang, W. Liu, J. Wang, High-resolution representations for labeling pixels and regions, (2019). arXiv preprint:1904.04514."},{"key":"10.1016\/j.dsp.2026.106278_bib0026","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"6881","article-title":"Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers","author":"Zheng","year":"2021"},{"key":"10.1016\/j.dsp.2026.106278_bib0027","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"7262","article-title":"Segmenter: transformer for semantic segmentation","author":"Strudel","year":"2021"},{"key":"10.1016\/j.dsp.2026.106278_bib0028","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"1290","article-title":"Masked-attention mask transformer for universal image segmentation","author":"Cheng","year":"2022"},{"key":"10.1016\/j.dsp.2026.106278_bib0029","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"12009","article-title":"Swin transformer v2: scaling up capacity and resolution","author":"Liu","year":"2022"},{"key":"10.1016\/j.dsp.2026.106278_bib0030","series-title":"Proceedings of the 1st ACM International Conference on Multimedia in Asia","first-page":"1","article-title":"Efficient dense modules of asymmetric convolution for real-time semantic segmentation","author":"Lo","year":"2019"},{"key":"10.1016\/j.dsp.2026.106278_bib0031","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"1296","article-title":"SwiftNet: real-time video object segmentation","author":"Wang","year":"2021"},{"key":"10.1016\/j.dsp.2026.106278_bib0032","series-title":"Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part I 16","first-page":"775","article-title":"Semantic flow for fast and accurate scene parsing","author":"Li","year":"2020"},{"key":"10.1016\/j.dsp.2026.106278_bib0033","doi-asserted-by":"crossref","first-page":"3051","DOI":"10.1007\/s11263-021-01515-2","article-title":"BiSeNet V2: bilateral network with guided aggregation for real-time semantic segmentation","volume":"129","author":"Yu","year":"2021","journal-title":"Int. J. Comput. Vis."},{"issue":"4","key":"10.1016\/j.dsp.2026.106278_bib0034","doi-asserted-by":"crossref","first-page":"4715","DOI":"10.1109\/TIV.2024.3363830","article-title":"LCFNets: compensation strategy for real-time semantic segmentation of autonomous driving","volume":"9","author":"Yang","year":"2024","journal-title":"IEEE Trans. Intell. Veh."},{"key":"10.1016\/j.dsp.2026.106278_bib0035","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"4258","article-title":"Perturbed and strict mean teachers for semi-supervised semantic segmentation","author":"Liu","year":"2022"},{"key":"10.1016\/j.dsp.2026.106278_bib0036","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"12566","article-title":"Severity-aware semantic segmentation with reinforced wasserstein training","author":"Liu","year":"2020"},{"key":"10.1016\/j.dsp.2026.106278_bib0037","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"5229","article-title":"Gated-SCNN: gated shape cnns for semantic segmentation","author":"Takikawa","year":"2019"},{"issue":"5","key":"10.1016\/j.dsp.2026.106278_bib0038","doi-asserted-by":"crossref","first-page":"3424","DOI":"10.1109\/TCSVT.2023.3325360","article-title":"BSSNet: a real-time semantic segmentation network for road scenes inspired from autoencoder","volume":"34","author":"Shi","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.dsp.2026.106278_bib0039","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"9145","article-title":"Partial order pruning: for best speed\/accuracy trade-off in neural architecture search","author":"Li","year":"2019"},{"key":"10.1016\/j.dsp.2026.106278_bib0040","series-title":"2021 IEEE International Conference on Robotics and Automation (ICRA)","first-page":"13517","article-title":"CABiNet: efficient context aggregation network for low-latency semantic segmentation","author":"Kumaar","year":"2021"},{"key":"10.1016\/j.dsp.2026.106278_bib0041","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"9716","article-title":"Rethinking bisenet for real-time semantic segmentation","author":"Fan","year":"2021"},{"key":"10.1016\/j.dsp.2026.106278_bib0042","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"4061","article-title":"HyperSeg: patch-wise hypernetwork for real-time semantic segmentation","author":"Nirkin","year":"2021"},{"key":"10.1016\/j.dsp.2026.106278_bib0043","unstructured":"J. Peng, Y. Liu, S. Tang, Y. Hao, L. Chu, G. Chen, Z. Wu, Z. Chen, Z. Yu, Y. Du, et al., PP-LiteSeg: a superior real-time semantic segmentation model, (2022). arXiv preprint:2204.02681."},{"issue":"10","key":"10.1016\/j.dsp.2026.106278_bib0044","doi-asserted-by":"crossref","first-page":"17224","DOI":"10.1109\/TITS.2022.3150350","article-title":"Deep multi-branch aggregation network for real-time semantic segmentation in street scenes","volume":"23","author":"Weng","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.dsp.2026.106278_bib0045","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.118537","article-title":"CSRNet: cascaded selective resolution network for real-time semantic segmentation","volume":"211","author":"Xiong","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.dsp.2026.106278_bib0046","series-title":"2023 International Joint Conference on Neural Networks (IJCNN)","first-page":"01","article-title":"Deep multi-resolution network for real-time semantic segmentation in street scenes","author":"Wang","year":"2023"},{"issue":"17","key":"10.1016\/j.dsp.2026.106278_bib0047","doi-asserted-by":"crossref","first-page":"28680","DOI":"10.1109\/JIOT.2024.3403174","article-title":"Multi-resolution refinement network for semantic segmentation in internet of things","volume":"11","author":"Wang","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.dsp.2026.106278_bib0048","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"6378","article-title":"SCTNet: single-branch cnn with transformer semantic information for real-time segmentation","volume":"Vol. 38","author":"Xu","year":"2024"},{"key":"10.1016\/j.dsp.2026.106278_bib0049","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.110487","article-title":"Parallel segmentation network for real-time semantic segmentation","volume":"148","author":"Chen","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.dsp.2026.106278_bib0050","article-title":"HFINet: heteroscale feature integration network for real-time semantic segmentation","volume":"138","author":"Shi","year":"2025","journal-title":"Signal Process.: Image Commun."},{"key":"10.1016\/j.dsp.2026.106278_bib0051","article-title":"HMSNet: Hilbert curve enhanced Mamba for real-time semantic segmentation","volume":"172","author":"Jia","year":"2025","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.dsp.2026.106278_bib0052","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"8818","article-title":"Temporally distributed networks for fast video semantic segmentation","author":"Hu","year":"2020"}],"container-title":["Digital Signal Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1051200426003969?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1051200426003969?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T00:50:11Z","timestamp":1780707011000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1051200426003969"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":52,"alternative-id":["S1051200426003969"],"URL":"https:\/\/doi.org\/10.1016\/j.dsp.2026.106278","relation":{},"ISSN":["1051-2004"],"issn-type":[{"value":"1051-2004","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"TSNet: Three-branch scale-aware network for real-time semantic segmentation","name":"articletitle","label":"Article Title"},{"value":"Digital Signal Processing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.dsp.2026.106278","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier Inc.","name":"copyright","label":"Copyright"}],"article-number":"106278"}}