{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T01:41:13Z","timestamp":1763343673353,"version":"3.45.0"},"reference-count":32,"publisher":"Tech Science Press","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CMC"],"published-print":{"date-parts":[[2025]]},"DOI":"10.32604\/cmc.2025.063507","type":"journal-article","created":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T04:20:53Z","timestamp":1748319653000},"page":"417-431","source":"Crossref","is-referenced-by-count":0,"title":["A Lane Coordinate Generation Model Utilizing Spatial Axis Attention and Multi-Scale Convolution"],"prefix":"10.32604","volume":"84","author":[{"given":"Duo","family":"Cui","sequence":"first","affiliation":[]},{"given":"Qiusheng","family":"Wang","sequence":"additional","affiliation":[]}],"member":"17807","published-online":{"date-parts":[[2025]]},"reference":[{"key":"ref1","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":"ref2","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops","first-page":"11","article-title":"The one hundred layers tiramisu: fully convolutional DenseNets for semantic segmentation","author":"J\u00e9gou","year":"2017"},{"key":"ref3","doi-asserted-by":"crossref","unstructured":"Chen L, Papandreou G, Schroff  F, Adam  H. Rethinking atrous convolution for semantic image segmentation. arXiv:1706.05587. 2017.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref4","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":"2018","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref5","unstructured":"Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, et al. MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861. 2017."},{"key":"ref6","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, et al. An image is worth 16 \u00d7 16 words: transformers for image recognition at scale. arXiv:2010.11929. 2020."},{"key":"ref7","series-title":"2018 IEEE Intelligent Vehicles Symposium (IV)","first-page":"286","article-title":"Towards end-to-end lane detection: an instance segmentation approach","author":"Neven","year":"2018"},{"key":"ref8","first-page":"454\u201361","article-title":"Robust lane detection based on convolutional neural network and random sample consensus","author":"Kim","year":"2014","journal-title":"Neural information processing"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"1088","DOI":"10.1109\/TITS.2012.2184756","article-title":"A learning approach towards detection and tracking of lane markings","volume":"13","author":"Gopalan","year":"2012","journal-title":"IEEE Trans Intell Transport Syst"},{"key":"ref10","unstructured":"Ho J, Kalchbrenner N, Weissenborn D, Salimans T. Axial attention in multidimensional transformers. arXiv:1912.12180. 2019."},{"key":"ref11","unstructured":"Chen T, Saxena S, Li L, Fleet DJ, Hinton GE. Pix2seq: a language modeling framework for object detection. arXiv:2109.10852. 2021."},{"key":"ref12","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. arXiv:1505.04597. 2015.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref13","unstructured":"Paszke A, Chaurasia A, Kim S, Culurciello E. ENet: a deep neural network architecture for real-time semantic segmentation. arXiv:1606.02147. 2016."},{"key":"ref14","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","article-title":"Spatial as deep: spatial CNN for traffic scene understanding","volume":"32","author":"Pan","year":"2018"},{"key":"ref15","doi-asserted-by":"crossref","unstructured":"Zheng T, Fang H, Zhang Y, Tang W, Yang Z, Liu H, et al. RESA: recurrent feature-shift aggregator for lane detection; 2021. arXiv:2008.13719. 2021.","DOI":"10.1609\/aaai.v35i4.16469"},{"key":"ref16","doi-asserted-by":"crossref","unstructured":"Liu L, Chen X, Zhu S, Tan P. CondLaneNet: a top-to-down lane detection framework based on conditional convolution. arXiv:2105.05003. 2021.","DOI":"10.1109\/ICCV48922.2021.00375"},{"key":"ref17","first-page":"5998","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref18","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, et al. Swin transformer: hierarchical vision transformer using shifted windows. arXiv:2103.14030. 2021.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref19","doi-asserted-by":"crossref","unstructured":"Hassani A, Walton S, Li J, Li S, Shi H. Neighborhood attention transformer. arXiv:2204.07143. 2023.","DOI":"10.1109\/CVPR52729.2023.00599"},{"key":"ref20","doi-asserted-by":"crossref","unstructured":"Valanarasu JMJ, Oza P, Hacihaliloglu I, Patel VM. Medical transformer: gated axial-attention for medical image segmentation. arXiv:2102.10662. 2021.","DOI":"10.1007\/978-3-030-87193-2_4"},{"key":"ref21","doi-asserted-by":"crossref","unstructured":"Wang H, Zhu Y, Green B, Adam H, Yuille AL, Chen L. Axial-DeepLab: Stand-alone axial-attention for panoptic segmentation; 2020. arXiv:2003.07853. 2020.","DOI":"10.1007\/978-3-030-58548-8_7"},{"key":"ref22","doi-asserted-by":"crossref","unstructured":"Zhou K. Lane2Seq: towards unified lane detection via sequence generation. arXiv:2402.17172. 2024.","DOI":"10.1109\/CVPR52733.2024.01603"},{"key":"ref23","unstructured":"TuSimple. TuSimple benchmark. [cited 2025 Mar 30]. Available from: https:\/\/github.com\/TuSimple\/tusimple-benchmark."},{"key":"ref24","unstructured":"Loshchilov I, Hutter F. Fixing weight decay regularization in adam. arXiv:1711.05101. 2017."},{"key":"ref25","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. arXiv:1512.03385. 2015.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref26","doi-asserted-by":"crossref","unstructured":"Qin Z, Wang H, Li X. Ultra fast structure-aware deep lane detection. 2020. arXiv:2004.11757.","DOI":"10.1007\/978-3-030-58586-0_17"},{"key":"ref27","doi-asserted-by":"crossref","unstructured":"Xiao L, Li X, Yang S, Yang W. ADNet: lane shape prediction via anchor decomposition. arXiv:2308.10481. 2023.","DOI":"10.1109\/ICCV51070.2023.00589"},{"key":"ref28","doi-asserted-by":"crossref","unstructured":"Tabelini L, Berriel R, Paix\u00e3o TM, Badue C, Souza AFD, Oliveira-Santos T. Keep your eyes on the lane: real-time attention-guided lane detection. arXiv:2010.12035. 2020.","DOI":"10.1109\/CVPR46437.2021.00036"},{"key":"ref29","doi-asserted-by":"crossref","unstructured":"Xu H, Wang S, Cai X, Zhang W, Liang X, Li Z. CurveLane-NAS: unifying lane-sensitive architecture search and adaptive point blending. arXiv:2007.12147. 2020.","DOI":"10.1007\/978-3-030-58555-6_41"},{"key":"ref30","doi-asserted-by":"crossref","unstructured":"Han J, Deng X, Cai X, Yang Z, Xu H, Xu C, et al. Laneformer: object-aware row-column transformers for lane detection. arXiv:2203.09830. 2022.","DOI":"10.1609\/aaai.v36i1.19961"},{"key":"ref31","doi-asserted-by":"crossref","first-page":"109053","DOI":"10.1016\/j.patcog.2022.109053","article-title":"Lane detection with versatile atrousformer and local semantic guidance","volume":"133","author":"Yang","year":"2023","journal-title":"Pattern Recognit"},{"key":"ref32","doi-asserted-by":"crossref","unstructured":"Wang J, Ma Y, Huang S, Hui T, Wang F, Qian C, et al. A keypoint-based global association network for lane detection. arXiv:2204.07335. 2022.","DOI":"10.1109\/CVPR52688.2022.00145"}],"container-title":["Computers, Materials &amp; Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/cdn.techscience.cn\/files\/cmc\/2025\/TSP_CMC-84-1\/TSP_CMC_63507\/TSP_CMC_63507.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T01:37:46Z","timestamp":1763343466000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v84n1\/61742"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":32,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025]]},"published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2025.063507","relation":{},"ISSN":["1546-2226"],"issn-type":[{"type":"electronic","value":"1546-2226"}],"subject":[],"published":{"date-parts":[[2025]]}}}