{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T22:41:52Z","timestamp":1769812912921,"version":"3.49.0"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T00:00:00Z","timestamp":1766361600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T00:00:00Z","timestamp":1766966400000},"content-version":"vor","delay-in-days":7,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Jiangsu Province Key R & D Program","award":["BE2023340"],"award-info":[{"award-number":["BE2023340"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61873086"],"award-info":[{"award-number":["61873086"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1007\/s40747-025-02191-2","type":"journal-article","created":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T10:37:34Z","timestamp":1766399854000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GDA-RoadSeg: an improved road segmentation network with gated depthwise attention feature fusion"],"prefix":"10.1007","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7130-8331","authenticated-orcid":false,"given":"Jianjun","family":"Ni","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenpu","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Gu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Simon X.","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,12,22]]},"reference":[{"issue":"9","key":"2191_CR1","doi-asserted-by":"publisher","first-page":"8083","DOI":"10.1109\/LRA.2024.3440093","volume":"9","author":"Z Wang","year":"2024","unstructured":"Wang Z, Jin Y, Deligiannis A, Fuentes-Michel J-C, Vossiek M (2024) Cross-modal supervision based road segmentation and trajectory prediction with automotive radar. IEEE Robotics and Automation Letters 9(9):8083\u20138089","journal-title":"IEEE Robotics and Automation Letters"},{"issue":"10","key":"2191_CR2","doi-asserted-by":"publisher","first-page":"17850","DOI":"10.1109\/JSEN.2025.3557426","volume":"25","author":"J Ni","year":"2025","unstructured":"Ni J, Chen Y, Tang G, Cao W, Yang SX (2025) An integration model of blind spot estimation and traversable area detection for indoor robots. IEEE Sens J 25(10):17850\u201317866","journal-title":"IEEE Sens J"},{"issue":"4","key":"2191_CR3","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1007\/s40747-024-01735-2","volume":"11","author":"Y Wu","year":"2025","unstructured":"Wu Y, Li Q (2025) Convnext embedded u-net for semantic segmentation in urban scenes of multi-scale targets. Complex & Intelligent Systems 11(4):181","journal-title":"Complex & Intelligent Systems"},{"issue":"3","key":"2191_CR4","doi-asserted-by":"publisher","first-page":"374","DOI":"10.20517\/ir.2023.22","volume":"3","author":"J Ni","year":"2023","unstructured":"Ni J, Chen Y, Tang G, Shi J, Cao WC, Shi P (2023) Deep learning-based scene understanding for autonomous robots: a survey. Intelligence & Robotics 3(3):374\u2013401","journal-title":"Intelligence & Robotics"},{"issue":"1","key":"2191_CR5","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1049\/ipr2.12948","volume":"18","author":"MI Ahmed","year":"2024","unstructured":"Ahmed MI, Foysal M, Chaity MD, Hossain ABMA (2024) Deeproadnet: A deep residual based segmentation network for road map detection from remote aerial image. IET Image Proc 18(1):265\u2013279","journal-title":"IET Image Proc"},{"issue":"3","key":"2191_CR6","doi-asserted-by":"publisher","first-page":"51","DOI":"10.33166\/AETiC.2024.03.004","volume":"8","author":"MS Hammoud","year":"2024","unstructured":"Hammoud MS, Lupin S (2024) Optimization of road detection using semantic segmentation and deep learning in self-driving cars. Annals of Emerging Technologies in Computing 8(3):51\u201363","journal-title":"Annals of Emerging Technologies in Computing"},{"issue":"3","key":"2191_CR7","doi-asserted-by":"publisher","first-page":"4311","DOI":"10.1007\/s40747-024-01364-9","volume":"10","author":"Y Zhang","year":"2024","unstructured":"Zhang Y, Zhang L, Wang Y, Xu W (2024) Agf-net: adaptive global feature fusion network for road extraction from remote-sensing images. Complex & Intelligent Systems 10(3):4311\u20134328","journal-title":"Complex & Intelligent Systems"},{"issue":"3","key":"2191_CR8","doi-asserted-by":"publisher","first-page":"230","DOI":"10.20517\/ir.2024.15","volume":"4","author":"Z Li","year":"2024","unstructured":"Li Z, Li M, Shi L, Li D (2024) A novel fatigue driving detection method based on whale optimization and attention-enhanced gru. Intelligence & Robotics 4(3):230\u201343","journal-title":"Intelligence & Robotics"},{"key":"2191_CR9","doi-asserted-by":"publisher","first-page":"5015115","DOI":"10.1109\/TIM.2025.3548062","volume":"74","author":"J Ni","year":"2025","unstructured":"Ni J, Chen Y, Zhang Z, Zhao Y, Yang SX (2025) An improved fuzzy decision and geometric inference-based indoor layout estimation model from rgb-d images. IEEE Trans Instrum Meas 74:5015115","journal-title":"IEEE Trans Instrum Meas"},{"key":"2191_CR10","first-page":"770","volume-title":"29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016","author":"K He","year":"2016","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016. Las Vegas, NV, USA, pp 770\u2013778"},{"key":"2191_CR11","doi-asserted-by":"crossref","unstructured":"Aly M (2008) Real time detection of lane markers in urban streets. In: 2008 IEEE Intelligent Vehicles Symposium, IV, Eindhoven, Netherlands, pp. 165\u2013170","DOI":"10.1109\/IVS.2008.4621152"},{"key":"2191_CR12","doi-asserted-by":"crossref","unstructured":"Wang B, Fr\u00e9mont V, Rodr\u00edguez SA (2014) Color-based road detection and its evaluation on the kitti road benchmark. In: 25th IEEE Intelligent Vehicles Symposium, IV 2014, Dearborn, MI, United states, pp. 31\u201336","DOI":"10.1109\/IVS.2014.6856619"},{"key":"2191_CR13","doi-asserted-by":"crossref","unstructured":"Yuan J, Tang S, Wang F, Zhang H (2014) A robust road segmentation method based on graph cut with learnable neighboring link weights. In: 2014 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014, Qingdao, China, pp. 1644\u20131649","DOI":"10.1109\/ITSC.2014.6957929"},{"key":"2191_CR14","doi-asserted-by":"crossref","unstructured":"Zhou S, Gong J, Xiong G, Chen H, Iagnemma K (2010)Road detection using support vector machine based on online learning and evaluation. In: 2010 IEEE Intelligent Vehicles Symposium, IV 2010, La Jolla, CA, United states, pp. 256\u2013261","DOI":"10.1109\/IVS.2010.5548086"},{"key":"2191_CR15","doi-asserted-by":"crossref","unstructured":"Alon Y, Ferencz A, Shashua A (2006) Off-road path following using region classification and geometric projection constraints. In: Proc. 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201906), vol. 1. New York, USA, pp. 689\u2013696","DOI":"10.1109\/CVPR.2006.213"},{"key":"2191_CR16","unstructured":"Vitor GB, Victorino AC, Ferreira JV (2014) A probabilistic distribution approach for the classification of urban roads in complex environments. In: Proc. IEEE Workshop on International Conference on Robotics and Automation, Hong Kong, China . IEEE"},{"key":"2191_CR17","doi-asserted-by":"crossref","unstructured":"Passani M, Yebes JJ, Bergasa LM (2014) CRF-based semantic labeling in miniaturized road scenes. In: Proc. 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), Qingdao, China, pp. 1902\u20131903","DOI":"10.1109\/ITSC.2014.6957977"},{"key":"2191_CR18","doi-asserted-by":"crossref","unstructured":"Xiao L, Dai B, Liu D, Hu T, Wu T (2015) CRF based road detection with multi-sensor fusion. In: Proc. 2015 IEEE Intelligent Vehicles Symposium (IV), Seoul, South Korea, pp. 192\u2013198","DOI":"10.1109\/IVS.2015.7225685"},{"issue":"8","key":"2191_CR19","doi-asserted-by":"publisher","first-page":"2749","DOI":"10.3390\/app10082749","volume":"10","author":"J Ni","year":"2020","unstructured":"Ni J, Chen Y, Chen Y, Zhu J, Ali D, Cao W (2020) A survey on theories and applications for self-driving cars based on deep learning methods. Applied Sciences-basel 10(8):2749","journal-title":"Applied Sciences-basel"},{"issue":"18","key":"2191_CR20","doi-asserted-by":"publisher","first-page":"6075","DOI":"10.3390\/s24186075","volume":"24","author":"Y Yuan","year":"2024","unstructured":"Yuan Y, Du Y, Ma Y, Lv H (2024) Dsc-net: Enhancing blind road semantic segmentation with visual sensor using a dual-branch swin-cnn architecture. Sensors 24(18):6075","journal-title":"Sensors"},{"issue":"2","key":"2191_CR21","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1007\/s13042-023-01927-1","volume":"15","author":"J Ni","year":"2024","unstructured":"Ni J, Zhang Z, Shen K, Tang G, Yang SX (2024) An improved deep network-based rgb-d semantic segmentation method for indoor scenes. Int J Mach Learn Cybern 15(2):589\u2013604","journal-title":"Int J Mach Learn Cybern"},{"key":"2191_CR22","doi-asserted-by":"crossref","unstructured":"Shafi I, Hussain I, Ahmad J, Kim PW, Choi GS, Ashraf I, Din S (2022) License plate identification and recognition in a non-standard environment using neural pattern matching. Complex & Intelligent Systems 8(5, SI), 3627\u20133639","DOI":"10.1007\/s40747-021-00419-5"},{"key":"2191_CR23","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: Proc. 18th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Munich, Germany, pp. 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"2191_CR24","doi-asserted-by":"crossref","unstructured":"Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: A deep convolutional encoder-decoder architecture for image segmentation 39(12), 2481\u20132495","DOI":"10.1109\/TPAMI.2016.2644615"},{"key":"2191_CR25","unstructured":"Chen L-C, Papandreou G, Schroff F, Adam H (2017) Rethinking atrous convolution for semantic image segmentation. arXiv e-prints, 1706\u201305587"},{"key":"2191_CR26","doi-asserted-by":"crossref","unstructured":"Fan R, Wang H, Cai P, Liu M (2020) Sne-roadseg: Incorporating surface normal information into semantic segmentation for accurate freespace detection. In: Proc. European Conference on Computer Vision, vol. 12375. Cham, pp. 340\u2013356","DOI":"10.1007\/978-3-030-58577-8_21"},{"issue":"1","key":"2191_CR27","first-page":"230","volume":"19","author":"Q Wang","year":"2017","unstructured":"Wang Q, Gao J, Yuan Y (2017) Embedding structured contour and location prior in siamesed fully convolutional networks for road detection 19(1):230\u2013241","journal-title":"Embedding structured contour and location prior in siamesed fully convolutional networks for road detection"},{"key":"2191_CR28","doi-asserted-by":"crossref","unstructured":"Sun J-Y, Kim S-W, Lee S-W, Kim Y-W, Ko S-J (2019) Reverse and boundary attention network for road segmentation. In: Proc. IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, South Korea, pp. 876\u2013885","DOI":"10.1109\/ICCVW.2019.00116"},{"key":"2191_CR29","doi-asserted-by":"crossref","unstructured":"Rosas-Arias L, Benitez-Garcia G, Portillo-Portillo J, Olivares-Mercado J, Sanchez-Perez G, Yanai K (2021) FASSD-net: Fast and accurate real-time semantic segmentation for embedded systems 23(9), 14349\u201314360","DOI":"10.1109\/TITS.2021.3127553"},{"issue":"11","key":"2191_CR30","first-page":"21505","volume":"23","author":"S Gong","year":"2022","unstructured":"Gong S, Zhou H, Xue F, Fang C, Li Y (2022) Zhou Y Fastroadseg: Fast monocular road segmentation network 23(11):21505\u201321514","journal-title":"Zhou Y Fastroadseg: Fast monocular road segmentation network"},{"key":"2191_CR31","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. In: 31st Annual Conference on Neural Information Processing Systems, NIPS 2017, vol. 30. Long Beach, CA"},{"issue":"7","key":"2191_CR32","doi-asserted-by":"publisher","first-page":"5163","DOI":"10.1109\/TIV.2024.3388726","volume":"9","author":"J Li","year":"2024","unstructured":"Li J, Zhang Y, Yun P, Zhou G, Chen Q, Fan R (2024) Roadformer: Duplex transformer for rgb-normal semantic road scene parsing. IEEE Transactions on Intelligent Vehicles 9(7):5163\u20135172","journal-title":"IEEE Transactions on Intelligent Vehicles"},{"key":"2191_CR33","doi-asserted-by":"crossref","unstructured":"Huang J, Li J, Jia N, Sun Y, Liu C, Chen Q, Fan R (2024) Roadformer+: Delivering rgb-x scene parsing through scale-aware information decoupling and advanced heterogeneous feature fusion. IEEE Transactions on Intelligent Vehicles","DOI":"10.1109\/TIV.2024.3448251"},{"key":"2191_CR34","doi-asserted-by":"crossref","unstructured":"Chen S, Han T, Zhang C, Liu W, Su J, Wang Z, Cai G (2024) Depth matters: Exploring deep interactions of rgb-d for semantic segmentation in traffic scenes. arXiv preprint arXiv:2409.07995","DOI":"10.1109\/IROS60139.2025.11246921"},{"key":"2191_CR35","doi-asserted-by":"crossref","unstructured":"Wang P, Chen P, Yuan Y, Liu D, Huang Z, Hou X, Cottrell G (2018)Understanding convolution for semantic segmentation. In: Proc. 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA, pp. 1451\u20131460","DOI":"10.1109\/WACV.2018.00163"},{"key":"2191_CR36","doi-asserted-by":"crossref","unstructured":"Cordts M, Omran M, Ramos S, Rehfeld T, Enzweiler M, Benenson R, Franke U, Roth S, Schiele B (2016) The cityscapes dataset for semantic urban scene understanding. In: Proc. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, pp. 3213\u20133223","DOI":"10.1109\/CVPR.2016.350"},{"key":"2191_CR37","doi-asserted-by":"crossref","unstructured":"Brostow GJ, Shotton J, Fauqueur J, Cipolla R (2008) Segmentation and recognition using structure from motion point clouds. In: Proc. Computer vision\u2013ECCV 2008: 10th European Conference on Computer Vision, Marseille, France, October 12-18, 2008, Proceedings, Part i 10, vol. 5302, pp. 44\u201357","DOI":"10.1007\/978-3-540-88682-2_5"},{"issue":"2","key":"2191_CR38","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.patrec.2008.04.005","volume":"30","author":"GJ Brostow","year":"2009","unstructured":"Brostow GJ, Fauqueur J, Cipolla R (2009) Semantic object classes in video: A high-definition ground truth database. Pattern Recogn Lett 30(2):88\u201397","journal-title":"Pattern Recogn Lett"},{"key":"2191_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.125497","volume":"263","author":"MH Amiri","year":"2025","unstructured":"Amiri MH, Hashjin NM, Najafabadi MK, Beheshti A, Khodadadi N (2025) An innovative data-driven ai approach for detecting and isolating faults in gas turbines at power plants. Expert Syst Appl 263:125497","journal-title":"Expert Syst Appl"},{"issue":"1","key":"2191_CR40","doi-asserted-by":"publisher","first-page":"5032","DOI":"10.1038\/s41598-024-54910-3","volume":"14","author":"MH Amiri","year":"2024","unstructured":"Amiri MH, Hashjin NM, Montazeri M, Mirjalili S, Khodadadi N (2024) Hippopotamus optimization algorithm: a novel nature-inspired optimization algorithm. Sci Rep 14(1):5032","journal-title":"Sci Rep"},{"key":"2191_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2025.114119","volume":"327","author":"N Mehrabi Hashjin","year":"2025","unstructured":"Mehrabi Hashjin N, Amiri MH, Beheshti A, Khanian Najafabadi M (2025) Q2ho-mftv: A binary hippopotamus optimization algorithm for feature selection with a brief review of binary optimization. Knowl-Based Syst 327:114119","journal-title":"Knowl-Based Syst"},{"key":"2191_CR42","doi-asserted-by":"crossref","unstructured":"Zohourian F, Antic B, Siegemund J, Meuter M, Pauli J (2018) Superpixel-based road segmentation for real-time systems using cnn. In: Proc. 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP), Funchal, Portugal, pp. 257\u2013265","DOI":"10.5220\/0006612002570265"},{"issue":"9","key":"2191_CR43","doi-asserted-by":"publisher","first-page":"5408","DOI":"10.1109\/LRA.2023.3295254","volume":"8","author":"E Milli","year":"2023","unstructured":"Milli E, Erkent \u00d6, Y\u0131lmaz AE (2023) Multi-modal multi-task (3mt) road segmentation. IEEE Robotics and Automation Letters 8(9):5408\u20135415","journal-title":"IEEE Robotics and Automation Letters"},{"key":"2191_CR44","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proc. 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, pp. 3431\u20133440","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"2191_CR45","doi-asserted-by":"crossref","unstructured":"Yadav S, Patra S, Arora C, Banerjee S (2017) Deep CNN with color lines model for unmarked road segmentation. In: Proc. 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China, pp. 585\u2013589","DOI":"10.1109\/ICIP.2017.8296348"},{"key":"2191_CR46","unstructured":"Guo M-H, Lu C-Z, Hou Q, Liu Z, Cheng M-M, Hu S-M (2022) Segnext: Rethinking convolutional attention design for semantic segmentation. In: 36th Conference on Neural Information Processing Systems, NeurIPS 2022, New Orleans, LA, United states"}],"container-title":["Complex & Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-025-02191-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-02191-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-02191-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T11:49:15Z","timestamp":1769773755000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-025-02191-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,22]]},"references-count":46,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["2191"],"URL":"https:\/\/doi.org\/10.1007\/s40747-025-02191-2","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,22]]},"assertion":[{"value":"13 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"53"}}