{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T17:46:50Z","timestamp":1777398410735,"version":"3.51.4"},"reference-count":34,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,3,20]],"date-time":"2023-03-20T00:00:00Z","timestamp":1679270400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Shandong Province","award":["ZR2022QF037"],"award-info":[{"award-number":["ZR2022QF037"]}]},{"name":"Natural Science Foundation of Shandong Province","award":["ZR2020QF108"],"award-info":[{"award-number":["ZR2020QF108"]}]},{"name":"Natural Science Foundation of Shandong Province","award":["ZR2020MF148"],"award-info":[{"award-number":["ZR2020MF148"]}]},{"name":"Natural Science Foundation of Shandong Province","award":["ZR2020QF046"],"award-info":[{"award-number":["ZR2020QF046"]}]},{"name":"Natural Science Foundation of Shandong Province","award":["300102342510"],"award-info":[{"award-number":["300102342510"]}]},{"name":"Natural Science Foundation of Shandong Province","award":["62072391"],"award-info":[{"award-number":["62072391"]}]},{"name":"Natural Science Foundation of Shandong Province","award":["62066013"],"award-info":[{"award-number":["62066013"]}]},{"name":"Natural Science Foundation of Shandong Province","award":["62103350"],"award-info":[{"award-number":["62103350"]}]},{"name":"Natural Science Foundation of Shandong Province","award":["2021ITA01020"],"award-info":[{"award-number":["2021ITA01020"]}]},{"name":"Fundamental Research Funds for the Central Universities, CHD","award":["ZR2022QF037"],"award-info":[{"award-number":["ZR2022QF037"]}]},{"name":"Fundamental Research Funds for the Central Universities, CHD","award":["ZR2020QF108"],"award-info":[{"award-number":["ZR2020QF108"]}]},{"name":"Fundamental Research Funds for the Central Universities, CHD","award":["ZR2020MF148"],"award-info":[{"award-number":["ZR2020MF148"]}]},{"name":"Fundamental Research Funds for the Central Universities, CHD","award":["ZR2020QF046"],"award-info":[{"award-number":["ZR2020QF046"]}]},{"name":"Fundamental Research Funds for the Central Universities, CHD","award":["300102342510"],"award-info":[{"award-number":["300102342510"]}]},{"name":"Fundamental Research Funds for the Central Universities, CHD","award":["62072391"],"award-info":[{"award-number":["62072391"]}]},{"name":"Fundamental Research Funds for the Central Universities, CHD","award":["62066013"],"award-info":[{"award-number":["62066013"]}]},{"name":"Fundamental Research Funds for the Central Universities, CHD","award":["62103350"],"award-info":[{"award-number":["62103350"]}]},{"name":"Fundamental Research Funds for the Central Universities, CHD","award":["2021ITA01020"],"award-info":[{"award-number":["2021ITA01020"]}]},{"name":"National Natural Science Foundation of China","award":["ZR2022QF037"],"award-info":[{"award-number":["ZR2022QF037"]}]},{"name":"National Natural Science Foundation of China","award":["ZR2020QF108"],"award-info":[{"award-number":["ZR2020QF108"]}]},{"name":"National Natural Science Foundation of China","award":["ZR2020MF148"],"award-info":[{"award-number":["ZR2020MF148"]}]},{"name":"National Natural Science Foundation of China","award":["ZR2020QF046"],"award-info":[{"award-number":["ZR2020QF046"]}]},{"name":"National Natural Science Foundation of China","award":["300102342510"],"award-info":[{"award-number":["300102342510"]}]},{"name":"National Natural Science Foundation of China","award":["62072391"],"award-info":[{"award-number":["62072391"]}]},{"name":"National Natural Science Foundation of China","award":["62066013"],"award-info":[{"award-number":["62066013"]}]},{"name":"National Natural Science Foundation of China","award":["62103350"],"award-info":[{"award-number":["62103350"]}]},{"name":"National Natural Science Foundation of China","award":["2021ITA01020"],"award-info":[{"award-number":["2021ITA01020"]}]},{"name":"China University Industry-University-Research Innovation Fund (New generation information technology innovation projects)","award":["ZR2022QF037"],"award-info":[{"award-number":["ZR2022QF037"]}]},{"name":"China University Industry-University-Research Innovation Fund (New generation information technology innovation projects)","award":["ZR2020QF108"],"award-info":[{"award-number":["ZR2020QF108"]}]},{"name":"China University Industry-University-Research Innovation Fund (New generation information technology innovation projects)","award":["ZR2020MF148"],"award-info":[{"award-number":["ZR2020MF148"]}]},{"name":"China University Industry-University-Research Innovation Fund (New generation information technology innovation projects)","award":["ZR2020QF046"],"award-info":[{"award-number":["ZR2020QF046"]}]},{"name":"China University Industry-University-Research Innovation Fund (New generation information technology innovation projects)","award":["300102342510"],"award-info":[{"award-number":["300102342510"]}]},{"name":"China University Industry-University-Research Innovation Fund (New generation information technology innovation projects)","award":["62072391"],"award-info":[{"award-number":["62072391"]}]},{"name":"China University Industry-University-Research Innovation Fund (New generation information technology innovation projects)","award":["62066013"],"award-info":[{"award-number":["62066013"]}]},{"name":"China University Industry-University-Research Innovation Fund (New generation information technology innovation projects)","award":["62103350"],"award-info":[{"award-number":["62103350"]}]},{"name":"China University Industry-University-Research Innovation Fund (New generation information technology innovation projects)","award":["2021ITA01020"],"award-info":[{"award-number":["2021ITA01020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Lane line detection is a fundamental and critical task for geographic information perception of driverless and advanced assisted driving. However, the traditional lane line detection method relies on manual adjustment of parameters, and has poor universality, a heavy workload, and poor robustness. Most deep learning-based methods make it difficult to effectively balance accuracy and efficiency. To improve the comprehensive perception ability of lane line geographic information in a natural traffic environment, a lane line detection algorithm based on a mixed-attention mechanism residual network (ResNet) and row anchor classification is proposed. A mixed-attention mechanism is added after the backbone network convolution, normalization and activation layers, respectively, so that the model can focus more on important lane line features to improve the pertinence and efficiency of feature extraction. In addition, to achieve faster detection speed and solve the problem of no vision, the method of lane line location selection and classification based on the row direction is used to detect whether there are lane lines in each candidate point according to the row anchor, reducing the high computational complexity caused by segmentation on a pixel-by-pixel basis of traditional semantic segmentation. Based on TuSimple and CurveLane datasets, multi-scene, multi-environment, multi-linear road image datasets and video sequences are integrated and self-built, and several experiments are designed and tested to verify the effectiveness of the proposed method. The test accuracy of the mixed-attention mechanism network model reached 95.96%, and the average time efficiency is nearly 180 FPS, which can achieve a high level of accuracy and real-time detection process. Therefore, the proposed method can meet the safety perception effect of lane line geographic information in natural traffic environments, and achieve an effective balance between the accuracy and efficiency of actual road application scenarios.<\/jats:p>","DOI":"10.3390\/ijgi12030132","type":"journal-article","created":{"date-parts":[[2023,3,21]],"date-time":"2023-03-21T02:36:22Z","timestamp":1679366182000},"page":"132","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["A Novel Lane Line Detection Algorithm for Driverless Geographic Information Perception Using Mixed-Attention Mechanism ResNet and Row Anchor Classification"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5737-368X","authenticated-orcid":false,"given":"Yongchao","family":"Song","sequence":"first","affiliation":[{"name":"School of Computer and Control Engineering, Yantai University, Yantai 264005, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer and Control Engineering, Yantai University, Yantai 264005, China"},{"name":"School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Fu","sequence":"additional","affiliation":[{"name":"Engineering Research Center of Highway Infrastructure Digitalization, Ministry of Education, Xi\u2019an 710064, China"},{"name":"College of Transportation Engineering, Chang\u2019an University, Xi\u2019an 710064, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yahong","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Transportation Engineering, Chang\u2019an University, Xi\u2019an 710064, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6688-5014","authenticated-orcid":false,"given":"Jindong","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer and Control Engineering, Yantai University, Yantai 264005, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4782-6796","authenticated-orcid":false,"given":"Jindong","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer and Control Engineering, Yantai University, Yantai 264005, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiqing","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Computer and Control Engineering, Yantai University, Yantai 264005, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7606-1411","authenticated-orcid":false,"given":"Xuan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer and Control Engineering, Yantai University, Yantai 264005, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,20]]},"reference":[{"key":"ref_1","first-page":"748","article-title":"Lane departure warning systems and lane line detection methods based on image processing and semantic segmentation: A review","volume":"7","author":"Chen","year":"2020","journal-title":"J. Traffic Transp. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Yu, T., Huang, H., Jiang, N., and Acharya, T.D. (2021). Study on Relative Accuracy and Verification Method of High-Definition Maps for Autonomous Driving. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10110761"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3254","DOI":"10.1109\/TITS.2017.2679222","article-title":"A Robust Lane Detection Method Based on Vanishing Point Estimation Using the Relevance of Line Segments","volume":"18","author":"Yoo","year":"2017","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1007\/s00138-015-0735-5","article-title":"Vision-based approach towards lane line detection and vehicle localization","volume":"27","author":"Du","year":"2016","journal-title":"Mach. Vis. Appl."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.compeleceng.2015.01.002","article-title":"A lane detection approach based on intelligent vision","volume":"42","author":"Yi","year":"2015","journal-title":"Comput. Electr. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1066","DOI":"10.1109\/TNNLS.2020.3039675","article-title":"Multitask Attention Network for Lane Detection and Fitting","volume":"33","author":"Wang","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"15526","DOI":"10.1109\/JSEN.2022.3187997","article-title":"Lane Detection Based on Two-Stage Noise Features Filtering and Clustering","volume":"22","author":"Wang","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6666","DOI":"10.1109\/TITS.2021.3060258","article-title":"Lane Detection Model Based on Spatio-Temporal Network with Double Convolutional Gated Recurrent Units","volume":"23","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.knosys.2012.01.002","article-title":"Linear fuzzy space based road lane model and detection","volume":"38","author":"Obradovic","year":"2013","journal-title":"Knowl.-Based Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1007\/s12239-012-0064-x","article-title":"Vision-based fusion of robust lane tracking and forward vehicle detection in a real driving environment","volume":"13","author":"Choi","year":"2012","journal-title":"Int. J. Automot. Technol."},{"key":"ref_11","unstructured":"Hou, Y., Ma, Z., Liu, C., and Loy, C.C. (November, January 7). Learning Lightweight Lane Detection CNNs by Self Attention Distillation. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Tabelini, L., Berriel, R., Paixao, T.M., Badue, C., De Souza, A.F., and Oliveira-Santos, T. (2021, January 20\u201325). Keep your eyes on the lane: Real-time attention-guided lane detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00036"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Xu, H., Wang, S., Cai, X., Zhang, W., Liang, X., and Li, Z. (2020, January 23\u201328). CurveLane-NAS: Unifying lane-sensitive architecture search and adaptive point blending. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58555-6_41"},{"key":"ref_14","first-page":"4405242","article-title":"A Real-Time Complex Road AI Perception Based on 5G-V2X for Smart City Security","volume":"2022","author":"Xu","year":"2022","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_15","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. Adv. Neural Inf. Process. Syst., 28."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Neven, D., De Brabandere, B., Georgoulis, S., Proesmans, M., and Van Gool, L. (2018, January 26\u201330). Towards end-to-end lane detection: An instance segmentation approach. Proceedings of the 2018 IEEE intelligent vehicles symposium, Changshu, China.","DOI":"10.1109\/IVS.2018.8500547"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1532","DOI":"10.1109\/TITS.2020.2971728","article-title":"Ripple-GAN: Lane Line Detection with Ripple Lane Line Detection Network and Wasserstein GAN","volume":"22","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, R., Yuan, Z., Liu, T., and Xiong, Z. (2021, January 3\u20138). End-to-end lane shape prediction with transformers. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV48630.2021.00374"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Tabelini, L., Berriel, R., Paixao, T.M., Badue, C., De Souza, A.F., and Oliveira-Santos, T. (2021, January 10\u201315). Polylanenet: Lane estimation via deep polynomial regression. Proceedings of the 2020 25th International Conference on Pattern Recognition, Milan, Italy.","DOI":"10.1109\/ICPR48806.2021.9412265"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Li, Q., Zhou, J., Li, B., Guo, Y., and Xiao, J. (2018). Robust Lane-Detection Method for Low-Speed Environments. Sensors, 18.","DOI":"10.3390\/s18124274"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"61","DOI":"10.5772\/58248","article-title":"Real-time Lane Detection on Suburban Streets Using Visual Cue Integration Regular Paper","volume":"11","author":"Fernando","year":"2014","journal-title":"Int. J. Adv. Robot. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4043","DOI":"10.1109\/TITS.2018.2791572","article-title":"Robust Lane Detection and Tracking for Real-Time Applications","volume":"19","author":"Lee","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1083","DOI":"10.1109\/TITS.2013.2252427","article-title":"Gradient-enhancing conversion for illumination-robust lane detection","volume":"14","author":"Yoo","year":"2013","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wei, X., Zhang, Z., Chai, Z., and Feng, W. (2018, January 24\u201327). Research on lane detection and tracking algorithm based on improved hough transform. Proceedings of the 2018 IEEE International Conference of Intelligent Robotic and Control Engineering (IRCE), Lanzhou, China.","DOI":"10.1109\/IRCE.2018.8492932"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.comcom.2015.08.010","article-title":"A real-time lane marking localization, tracking and communication system","volume":"73","author":"Mammeri","year":"2016","journal-title":"Comput. Commun."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhao, K., Meuter, M., Nunn, C., M\u00fcller, D., M\u00fcller-Schneiders, S., and Pauli, J. (2012, January 3\u20137). A novel multi-lane detection and tracking system. Proceedings of the 2012 IEEE Intelligent Vehicles Symposium, Madrid, Spain.","DOI":"10.1109\/IVS.2012.6232168"},{"key":"ref_28","first-page":"276","article-title":"Study on curved Lane Detection Using Catmull-Rom Spline","volume":"5","author":"He","year":"2015","journal-title":"Chin. J. Automot. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1656","DOI":"10.1177\/0954407014567719","article-title":"Probabilistic lane detection and lane tracking for autonomous vehicles using a cascade particle filter","volume":"229","author":"Lee","year":"2015","journal-title":"Proc. Inst. Mech. Eng. Part D J. Automob. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"107623","DOI":"10.1016\/j.patcog.2020.107623","article-title":"A review of lane detection methods based on deep learning","volume":"111","author":"Tang","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1109\/TVT.2019.2949603","article-title":"Robust lane detection from continuous driving scenes using deep neural networks","volume":"69","author":"Zou","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1016\/j.neucom.2020.06.094","article-title":"Deep reinforcement learning based lane detection and localization","volume":"413","author":"Zhao","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Qin, Z., Wang, H., and Li, X. (2020, January 23\u201328). Ultra fast structure-aware deep lane detection. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58586-0_17"},{"key":"ref_34","unstructured":"(2018, October 20). TuSimple: Tusimple Benchmark. Available online: https:\/\/github.com\/TuSimple\/tusimple-benchmark."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/12\/3\/132\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:59:47Z","timestamp":1760122787000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/12\/3\/132"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,20]]},"references-count":34,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["ijgi12030132"],"URL":"https:\/\/doi.org\/10.3390\/ijgi12030132","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,20]]}}}