{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T15:03:26Z","timestamp":1753887806777,"version":"3.41.2"},"reference-count":36,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,12,7]],"date-time":"2021-12-07T00:00:00Z","timestamp":1638835200000},"content-version":"vor","delay-in-days":340,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004480","name":"Natural Science Foundation of Shanxi Province","doi-asserted-by":"publisher","award":["201901D111467"],"award-info":[{"award-number":["201901D111467"]}],"id":[{"id":"10.13039\/501100004480","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>The use of multimodal sensors for lane line segmentation has become a growing trend. To achieve robust multimodal fusion, we introduced a new multimodal fusion method and proved its effectiveness in an improved fusion network. Specifically, a multiscale fusion module is proposed to extract effective features from data of different modalities, and a channel attention module is used to adaptively calculate the contribution of the fused feature channels. We verified the effect of multimodal fusion on the KITTI benchmark dataset and A2D2 dataset and proved the effectiveness of the proposed method on the enhanced KITTI dataset. Our method achieves robust lane line segmentation, which is 4.53% higher than the direct fusion on the precision index, and obtains the highest F2 score of 79.72%. We believe that our method introduces an optimization idea of modal data structure level for multimodal fusion.<\/jats:p>","DOI":"10.1155\/2021\/6791882","type":"journal-article","created":{"date-parts":[[2021,12,8]],"date-time":"2021-12-08T00:20:08Z","timestamp":1638922808000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multiscale Efficient Channel Attention for Fusion Lane Line Segmentation"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8413-123X","authenticated-orcid":false,"given":"Kang","family":"Liu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5677-0328","authenticated-orcid":false,"given":"Xin","family":"Gao","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,12,7]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"crossref","unstructured":"GarnettN. CohenR. Pe\u2019erT. LahavR. andLeviD. 3d-lanenet: end-to-end 3d multiple lane detection Proceedings of the IEEE\/CVF International Conference on Computer Vision November 2019 Seoul Korea (South) 2921\u20132930 https:\/\/doi.org\/10.1109\/iccv.2019.00301.","DOI":"10.1109\/ICCV.2019.00301"},{"key":"e_1_2_9_2_2","doi-asserted-by":"crossref","unstructured":"ZhangJ. XuYi NiB. andDuanZ. Geometric constrained joint lane segmentation and lane boundary detection Proceedings of the european conference on computer vision (ECCV) October 2018 Munich Germany 486\u2013502 https:\/\/doi.org\/10.1007\/978-3-030-01246-5_30 2-s2.0-85055094665.","DOI":"10.1007\/978-3-030-01246-5_30"},{"key":"e_1_2_9_3_2","unstructured":"WangZe RenW. andQiuQ. Lanenet: real-time lane detection networks for autonomous driving https:\/\/arxiv.org\/abs\/1807.01726."},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/9180391"},{"key":"e_1_2_9_5_2","article-title":"Medical image segmentation algorithm based on optimized convolutional neural network-adaptive dropout depth calculation","volume":"2020","author":"Feng-Ping An","year":"2020","journal-title":"Complexity"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2020.3030767"},{"key":"e_1_2_9_7_2","doi-asserted-by":"crossref","unstructured":"TongL. ChenZ. YangYi WuZ. andLiH. Lane detection in low-light conditions using an efficient data enhancement: light conditions style transfer Proceedings of the 2020 IEEE Intelligent Vehicles Symposium (IV) November 2020 Las Vegas NV USA IEEE 1394\u20131399 https:\/\/doi.org\/10.1109\/iv47402.2020.9304613.","DOI":"10.1109\/IV47402.2020.9304613"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2019.2892405"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2020.3023541"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/3089189"},{"key":"e_1_2_9_11_2","doi-asserted-by":"crossref","unstructured":"GuS. ZhangY. TangJ. YangJ. andKongH. Road detection through crf based lidar-camera fusion Proceedings of the 2019 International Conference on Robotics and Automation (ICRA) May 2019 Montreal QC Canada IEEE 3832\u20133838 https:\/\/doi.org\/10.1109\/icra.2019.8793585 2-s2.0-85071428699.","DOI":"10.1109\/ICRA.2019.8793585"},{"key":"e_1_2_9_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.robot.2018.11.002"},{"key":"e_1_2_9_13_2","doi-asserted-by":"crossref","unstructured":"WangQ. WuB. ZhuP. LiP. ZuoW. andHuQ. Eca-net: efficient channel attention for deep convolutional neural networks Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) June 2020 Seattle WA USA https:\/\/doi.org\/10.1109\/cvpr42600.2020.01155.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"e_1_2_9_14_2","doi-asserted-by":"crossref","unstructured":"DaigavaneP. M.andBajajP. R. Road lane detection with improved canny edges using ant colony optimization Proceedings of the 2010 3rd International Conference on Emerging Trends in Engineering and Technology November 2010 Goa India IEEE 76\u201380 https:\/\/doi.org\/10.1109\/icetet.2010.128.","DOI":"10.1109\/ICETET.2010.128"},{"key":"e_1_2_9_15_2","doi-asserted-by":"crossref","unstructured":"Van GansbekeW. De BrabandereB. NevenD. ProesmansM. andVan GoolL. End-to-end lane detection through differentiable least-squares fitting Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops October 2019 Seoul Korea (South) https:\/\/doi.org\/10.1109\/iccvw.2019.00119.","DOI":"10.1109\/ICCVW.2019.00119"},{"key":"e_1_2_9_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2021.3088488"},{"key":"e_1_2_9_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/jsen.2018.2832291"},{"key":"e_1_2_9_18_2","doi-asserted-by":"crossref","unstructured":"PanX. ShiJ. LuoP. WangX. andTangX. Spatial as deep: spatial cnn for traffic scene understanding Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence February 2018 Louisiana USA.","DOI":"10.1609\/aaai.v32i1.12301"},{"key":"e_1_2_9_19_2","doi-asserted-by":"crossref","unstructured":"HeB. RuiAi YangY. andLangX. Accurate and robust lane detection based on dual-view convolutional neutral network Proceedings of the 2016 IEEE Intelligent Vehicles Symposium (IV) June 2016 Gothenburg Sweden IEEE 1041\u20131046 https:\/\/doi.org\/10.1109\/ivs.2016.7535517 2-s2.0-84983412497.","DOI":"10.1109\/IVS.2016.7535517"},{"key":"e_1_2_9_20_2","doi-asserted-by":"crossref","unstructured":"ChoH. SeoY.-W. Vijaya KumarB. V. K. andRaj RajkumarR. A multi-sensor fusion system for moving object detection and tracking in urban driving environments Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA) June 2014 Hong Kong China IEEE 1836\u20131843 https:\/\/doi.org\/10.1109\/icra.2014.6907100 2-s2.0-84929193302.","DOI":"10.1109\/ICRA.2014.6907100"},{"key":"e_1_2_9_21_2","doi-asserted-by":"crossref","unstructured":"HanMa MaY. JiaoJ. BhuttaM. U. M. BocusM. J. WangL. LiuM. andFanR. Multiple lane detection algorithm based on optimised dense disparity map estimation Proceedings of the 2018 IEEE International Conference on Imaging Systems and Techniques (IST) October 2018 Krakow Poland IEEE 1\u20135.","DOI":"10.1109\/IST.2018.8577122"},{"key":"e_1_2_9_22_2","doi-asserted-by":"crossref","unstructured":"EitelA. Tobias SpringenbergJ. SpinelloL. RiedmillerM. andBurgardW. Multimodal deep learning for robust rgb-d object recognition Proceedings of the 2015 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) October 2015 Hamburg Germany IEEE 681\u2013687 https:\/\/doi.org\/10.1109\/iros.2015.7353446 2-s2.0-84958157379.","DOI":"10.1109\/IROS.2015.7353446"},{"key":"e_1_2_9_23_2","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/6271348"},{"key":"e_1_2_9_24_2","doi-asserted-by":"crossref","unstructured":"Joel Schlosser ChowC. K. andKiraZ. Fusing lidar and images for pedestrian detection using convolutional neural networks Proceedings of the2016 IEEE International Conference on Robotics and Automation (ICRA) May 2016 Stockholm Sweden IEEE 2198\u20132205 https:\/\/doi.org\/10.1109\/icra.2016.7487370 2-s2.0-84977470268.","DOI":"10.1109\/ICRA.2016.7487370"},{"key":"e_1_2_9_25_2","first-page":"5998","volume-title":"Advances in Neural Information Processing Systems","author":"Vaswani A.","year":"2017"},{"key":"e_1_2_9_26_2","doi-asserted-by":"crossref","unstructured":"FuJ. ZhengH. andMeiT. Look closer to see better: recurrent attention convolutional neural network for fine-grained image recognition Proceedings of the IEEE conference on computer vision and pattern recognition May 2017 Honolulu HI USA 4438\u20134446 https:\/\/doi.org\/10.1109\/cvpr.2017.476 2-s2.0-85041918224.","DOI":"10.1109\/CVPR.2017.476"},{"key":"e_1_2_9_27_2","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/5836596"},{"key":"e_1_2_9_28_2","doi-asserted-by":"crossref","unstructured":"WangF. JiangM. QianC. YangS. LiC. ZhangH. WangX. andTangX. Residual attention network for image classification Proceedings of the IEEE conference on computer vision and pattern recognition July 2017 Honolulu HI USA 3156\u20133164 https:\/\/doi.org\/10.1109\/cvpr.2017.683 2-s2.0-85044522649.","DOI":"10.1109\/CVPR.2017.683"},{"key":"e_1_2_9_29_2","doi-asserted-by":"crossref","unstructured":"ChenL.-C. YangYi WangJ. XuW. andYuilleA. L. Attention to scale: scale-aware semantic image segmentation Proceedings of the IEEE conference on computer vision and pattern recognition June 2016 Las Vegas NV USA 3640\u20133649 https:\/\/doi.org\/10.1109\/cvpr.2016.396 2-s2.0-84986244054.","DOI":"10.1109\/CVPR.2016.396"},{"key":"e_1_2_9_30_2","doi-asserted-by":"crossref","unstructured":"RonnebergerO. FischerP. andBroxT. U-net: convolutional networks for biomedical image segmentation Proceedings of the International Conference on Medical image computing and computer-assisted intervention May 2015 Munich Germany Springer 234\u2013241 https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28 2-s2.0-84951834022.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_2_9_31_2","unstructured":"XiangLi WangW. HuX. andYangJ. Selective kernel networks Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition June 2019 Long Beach CA USA 510\u2013519."},{"key":"e_1_2_9_32_2","doi-asserted-by":"crossref","unstructured":"GeigerA. LenzP. andUrtasunR. Are we ready for autonomous driving? the kitti vision benchmark suite Proceedings of the 2012 IEEE conference on computer vision and pattern recognition June 2012 Providence RI USA IEEE 3354\u20133361 https:\/\/doi.org\/10.1109\/cvpr.2012.6248074 2-s2.0-84866704163.","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"e_1_2_9_33_2","unstructured":"GeyerJ. KassahunY. MahmudiM. RicouX. DurgeshR. ChungA. S. andHauswaldL. A2d2: audi autonomous driving dataset 2020 https:\/\/arxiv.org\/abs\/2004.06320."},{"key":"e_1_2_9_34_2","unstructured":"KingmaD. P.andJimmyBa Adam: a method for stochastic optimization 2014 https:\/\/arxiv.org\/abs\/1412.6980."},{"key":"e_1_2_9_35_2","doi-asserted-by":"crossref","unstructured":"HouY. ZhengM. LiuC. andLoyC. C. Learning lightweight lane detection cnns by self attention distillation Proceedings of the IEEE\/CVF international conference on computer vision November 2019 Seoul Korea (South) 1013\u20131021 https:\/\/doi.org\/10.1109\/iccv.2019.00110.","DOI":"10.1109\/ICCV.2019.00110"},{"key":"e_1_2_9_36_2","unstructured":"SimonyanK.andZissermanA. Very deep convolutional networks for large-scale image recognition 2014 https:\/\/arxiv.org\/abs\/1409.1556."}],"container-title":["Complexity"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2021\/6791882.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2021\/6791882.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2021\/6791882","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,9]],"date-time":"2024-08-09T22:47:05Z","timestamp":1723243625000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2021\/6791882"}},"subtitle":[],"editor":[{"given":"Chao","family":"Zeng","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":36,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["10.1155\/2021\/6791882"],"URL":"https:\/\/doi.org\/10.1155\/2021\/6791882","archive":["Portico"],"relation":{},"ISSN":["1076-2787","1099-0526"],"issn-type":[{"type":"print","value":"1076-2787"},{"type":"electronic","value":"1099-0526"}],"subject":[],"published":{"date-parts":[[2021,1]]},"assertion":[{"value":"2021-08-10","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-11-23","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-12-07","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"6791882"}}