{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,28]],"date-time":"2025-08-28T12:09:21Z","timestamp":1756382961521,"version":"3.40.5"},"reference-count":21,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,12,7]],"date-time":"2021-12-07T00:00:00Z","timestamp":1638835200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Mobile Information Systems"],"published-print":{"date-parts":[[2021,12,7]]},"abstract":"<jats:p>In the automatic lane-keeping system (ALKS), the vehicle must stably and accurately detect the boundary of its current lane for precise positioning. At present, the detection accuracy of the lane algorithm based on deep learning has a greater leap than that of the traditional algorithm, and it can achieve better recognition results for corners and occlusion situations. However, mainstream algorithms are difficult to balance between accuracy and efficiency. In response to this situation, we propose a single-step method that directly outputs lane shape model parameters. This method uses MobileNet v2 and spatial CNN (SCNN) to construct a network to quickly extract lane features and learn global context information. Then, through depth polynomial regression, a polynomial representing each lane mark in the image is output. Finally, the proposed method was verified in the TuSimple dataset. Compared with existing algorithms, it achieves a balance between accuracy and efficiency. Experiments show that the recognition accuracy and detection speed of our method in the same environment have reached the level of mainstream algorithms, and an effective balance has been achieved between the two.<\/jats:p>","DOI":"10.1155\/2021\/1099434","type":"journal-article","created":{"date-parts":[[2021,12,7]],"date-time":"2021-12-07T20:35:14Z","timestamp":1638909314000},"page":"1-5","source":"Crossref","is-referenced-by-count":2,"title":["A Network That Balances Accuracy and Efficiency for Lane Detection"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7120-659X","authenticated-orcid":true,"given":"Ce","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Mechanical Engineering, Tianjin University of Science & Technology, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Han","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Tianjin University of Science & Technology, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dan","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Tianjin University of Science & Technology, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Qiao","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Tianjin University of Science & Technology, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7066-3289","authenticated-orcid":true,"given":"Yier","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Tianjin University of Science & Technology, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"first-page":"11582","article-title":"Fastdraw: addressing the long tail of lane detection by adapting a sequential prediction network","author":"J. Philion","key":"1"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2017.08.014"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2015.12.010"},{"first-page":"286","article-title":"Towards end-to-end lane detection: an instance segmentation approach","author":"D. Neven","key":"4"},{"article-title":"Key points estimation and point instance segmentation approach for lane detection","year":"2020","author":"Y. M. Ko","key":"5"},{"first-page":"7276","article-title":"Spatial as deep: spatial CNN for traffic scene understanding","author":"X. Pan","key":"6"},{"first-page":"256","article-title":"EL-GAN: embedding loss driven generative adversarial networks for lane detection","author":"M. Ghafoorian","key":"7"},{"first-page":"502","article-title":"Geometric constrained joint lane segmentation and lane boundary detection","author":"J. Zhang","key":"8"},{"article-title":"Polylanenet: lane estimation via deep polynomial regression","year":"2020","author":"L. T. Torres","key":"9"},{"first-page":"4510","article-title":"Mobilenetv2: inverted residuals and linear bottlenecks","author":"M. Sandler","key":"10"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1007\/s00138-011-0404-2"},{"first-page":"2334","article-title":"Real-time lane detection by using multiple cues","author":"Z. Teng","key":"12"},{"first-page":"7","article-title":"Lane extraction and quality evaluation: a Hough transform based approach","author":"X. Wang","key":"13"},{"article-title":"VPGNet: vanishing point guided network for lane and road marking detection and recognition","year":"2017","author":"S. Lee","key":"14"},{"first-page":"1013","article-title":"Learning lightweight lane detection CNNs by self attention distillation","author":"Y. Hou","key":"15"},{"author":"H. Xu","key":"16","article-title":"CurveLane-NAS: unifying lane-sensitive architecture search and adaptive point blending"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2019.2930731"},{"key":"18"},{"first-page":"770","article-title":"Deep residual learning for image recognition","author":"K. He","key":"19"},{"article-title":"Tusimple dataset","year":"2017","author":"TuSimple","key":"20"},{"first-page":"248","article-title":"ImageNet: a large-scale hierarchical image database","author":"J. Deng","key":"21"}],"container-title":["Mobile Information Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/misy\/2021\/1099434.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/misy\/2021\/1099434.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/misy\/2021\/1099434.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,7]],"date-time":"2021-12-07T20:35:16Z","timestamp":1638909316000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/misy\/2021\/1099434\/"}},"subtitle":[],"editor":[{"given":"Fazlullah","family":"Khan","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2021,12,7]]},"references-count":21,"alternative-id":["1099434","1099434"],"URL":"https:\/\/doi.org\/10.1155\/2021\/1099434","relation":{},"ISSN":["1875-905X","1574-017X"],"issn-type":[{"type":"electronic","value":"1875-905X"},{"type":"print","value":"1574-017X"}],"subject":[],"published":{"date-parts":[[2021,12,7]]}}}