{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,3]],"date-time":"2026-05-03T07:49:03Z","timestamp":1777794543307,"version":"3.51.4"},"reference-count":68,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Displays"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.displa.2026.103487","type":"journal-article","created":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T23:30:39Z","timestamp":1776814239000},"page":"103487","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Multi-grained enhancement with boundary uncertainty loss for road and lane segmentation"],"prefix":"10.1016","volume":"94","author":[{"given":"Xin","family":"Gao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenhui","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yijin","family":"Xiong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianwang","family":"Gan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingwen","family":"Meng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoying","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianqiang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"3","key":"10.1016\/j.displa.2026.103487_b1","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1109\/JAS.2019.1911459","article-title":"Progressive lidar adaptation for road detection","volume":"6","author":"Chen","year":"2019","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"10.1016\/j.displa.2026.103487_b2","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2022.105568","article-title":"Robust lane line segmentation based on group feature enhancement","volume":"117","author":"Gao","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.displa.2026.103487_b3","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.110539","article-title":"Tube-LaneNet: Predict each three-dimensional lane as a completed structure via geometric priors","volume":"149","author":"Kou","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.displa.2026.103487_b4","doi-asserted-by":"crossref","DOI":"10.1016\/j.displa.2025.103179","article-title":"Exploiting independent query information for few-shot image segmentation","volume":"91","author":"Liu","year":"2026","journal-title":"Displays"},{"issue":"11","key":"10.1016\/j.displa.2026.103487_b5","doi-asserted-by":"crossref","first-page":"21405","DOI":"10.1109\/TITS.2022.3177615","article-title":"SFNet-N: An improved sfnet algorithm for semantic segmentation of low-light autonomous driving road scenes","volume":"23","author":"Wang","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"4","key":"10.1016\/j.displa.2026.103487_b6","doi-asserted-by":"crossref","first-page":"7477","DOI":"10.1109\/LRA.2021.3098066","article-title":"Laneaf: Robust multi-lane detection with affinity fields","volume":"6","author":"Abualsaud","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"issue":"12","key":"10.1016\/j.displa.2026.103487_b7","doi-asserted-by":"crossref","first-page":"15341","DOI":"10.1109\/TVT.2023.3296735","article-title":"s: A multi-task detection model based on image processing for autonomous driving scenarios","volume":"72","author":"Zhao","year":"2023","journal-title":"IEEE Trans. Veh. Technol."},{"key":"10.1016\/j.displa.2026.103487_b8","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.109473","article-title":"Enhanced cross layer refinement network for robust lane detection across diverse lighting and road conditions","volume":"139","author":"Dai","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.displa.2026.103487_b9","series-title":"2022 International Conference on Robotics and Automation","first-page":"11124","article-title":"Fast road segmentation via uncertainty-aware symmetric network","author":"Chang","year":"2022"},{"key":"10.1016\/j.displa.2026.103487_b10","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.111002","article-title":"A customized multi-class pavement distress segmentation method for routine repair monitoring","volume":"154","author":"Wang","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.displa.2026.103487_b11","doi-asserted-by":"crossref","DOI":"10.1016\/j.displa.2022.102332","article-title":"Dual geometric perception for cross-domain road segmentation","volume":"76","author":"Zou","year":"2023","journal-title":"Displays"},{"key":"10.1016\/j.displa.2026.103487_b12","doi-asserted-by":"crossref","unstructured":"T. Zheng, H. Fang, Y. Zhang, W. Tang, Z. Yang, H. Liu, D. Cai, Resa: Recurrent feature-shift aggregator for lane detection, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35, 2021, pp. 3547\u20133554.","DOI":"10.1609\/aaai.v35i4.16469"},{"key":"10.1016\/j.displa.2026.103487_b13","doi-asserted-by":"crossref","DOI":"10.1016\/j.displa.2024.102902","article-title":"LPCNet: End-to-end lane detection with PnP compression and condition DETR","volume":"87","author":"Lu","year":"2025","journal-title":"Displays"},{"key":"10.1016\/j.displa.2026.103487_b14","first-page":"1","article-title":"Yolop: You only look once for panoptic driving perception","author":"Wu","year":"2022","journal-title":"Mach. Intell. Res."},{"key":"10.1016\/j.displa.2026.103487_b15","series-title":"Hybridnets: End-to-end perception network","author":"Vu","year":"2022"},{"key":"10.1016\/j.displa.2026.103487_b16","series-title":"Yolopv2: Better, faster, stronger for panoptic driving perception","author":"Han","year":"2022"},{"key":"10.1016\/j.displa.2026.103487_b17","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2023.110152","article-title":"YOLOPX: Anchor-free multi-task learning network for panoptic driving perception","volume":"148","author":"Zhan","year":"2024","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.displa.2026.103487_b18","doi-asserted-by":"crossref","unstructured":"J. Zhang, Y. Xu, B. Ni, Z. Duan, Geometric constrained joint lane segmentation and lane boundary detection, in: Proceedings of the European Conference on Computer Vision, ECCV, 2018, pp. 486\u2013502.","DOI":"10.1007\/978-3-030-01246-5_30"},{"issue":"8","key":"10.1016\/j.displa.2026.103487_b19","doi-asserted-by":"crossref","first-page":"5331","DOI":"10.1109\/TCSVT.2022.3144184","article-title":"Bayesian gabor network with uncertainty estimation for pedestrian lane detection in assistive navigation","volume":"32","author":"Le","year":"2022","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"6","key":"10.1016\/j.displa.2026.103487_b20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3530836","article-title":"Grod: Deep learning with gradients orthogonal decomposition for knowledge transfer, distillation, and adversarial training","volume":"16","author":"Xiong","year":"2022","journal-title":"ACM Trans. Knowl. Discov. Data (TKDD)"},{"key":"10.1016\/j.displa.2026.103487_b21","article-title":"A survey of collaborative perception in intelligent vehicles at intersections","author":"Gao","year":"2024","journal-title":"IEEE Trans. Intell. Veh."},{"key":"10.1016\/j.displa.2026.103487_b22","doi-asserted-by":"crossref","unstructured":"J.-Y. Sun, S.-W. Kim, S.-W. Lee, Y.-W. Kim, S.-J. Ko, Reverse and boundary attention network for road segmentation, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops, 2019.","DOI":"10.1109\/ICCVW.2019.00116"},{"key":"10.1016\/j.displa.2026.103487_b23","series-title":"2018 IEEE Intelligent Vehicles Symposium","first-page":"1013","article-title":"Multinet: Real-time joint semantic reasoning for autonomous driving","author":"Teichmann","year":"2018"},{"key":"10.1016\/j.displa.2026.103487_b24","series-title":"Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XXX","first-page":"340","article-title":"Sne-roadseg: Incorporating surface normal information into semantic segmentation for accurate freespace detection","volume":"Vol. 16","author":"Fan","year":"2020"},{"issue":"10","key":"10.1016\/j.displa.2026.103487_b25","doi-asserted-by":"crossref","first-page":"10750","DOI":"10.1109\/TCYB.2021.3064089","article-title":"Dynamic fusion module evolves drivable area and road anomaly detection: A benchmark and algorithms","volume":"52","author":"Wang","year":"2021","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.displa.2026.103487_b26","doi-asserted-by":"crossref","DOI":"10.1016\/j.displa.2024.102787","article-title":"A pyramid auxiliary supervised U-net model for road crack detection with dual-attention mechanism","volume":"84","author":"Lu","year":"2024","journal-title":"Displays"},{"key":"10.1016\/j.displa.2026.103487_b27","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2021.108020","article-title":"Channel attention in LiDAR-camera fusion for lane line segmentation","volume":"118","author":"Zhang","year":"2021","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.displa.2026.103487_b28","doi-asserted-by":"crossref","unstructured":"X. Pan, J. Shi, P. Luo, X. Wang, X. Tang, Spatial as deep: Spatial cnn for traffic scene understanding, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32, 2018.","DOI":"10.1609\/aaai.v32i1.12301"},{"key":"10.1016\/j.displa.2026.103487_b29","series-title":"Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XXIV","first-page":"276","article-title":"Ultra fast structure-aware deep lane detection","volume":"Vol. 16","author":"Qin","year":"2020"},{"key":"10.1016\/j.displa.2026.103487_b30","series-title":"2018 IEEE Intelligent Vehicles Symposium","first-page":"286","article-title":"Towards end-to-end lane detection: an instance segmentation approach","author":"Neven","year":"2018"},{"issue":"4","key":"10.1016\/j.displa.2026.103487_b31","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","article-title":"Fully convolutional networks for semantic segmentation","volume":"39","author":"Shelhamer","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.displa.2026.103487_b32","doi-asserted-by":"crossref","unstructured":"L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, H. Adam, Encoder-decoder with atrous separable convolution for semantic image segmentation, in: Proceedings of the European Conference on Computer Vision, ECCV, 2018, pp. 801\u2013818.","DOI":"10.1007\/978-3-030-01234-2_49"},{"issue":"1","key":"10.1016\/j.displa.2026.103487_b33","doi-asserted-by":"crossref","DOI":"10.1049\/itr2.70114","article-title":"Exploiting image enhancement and edge detection for low-light road segmentation","volume":"19","author":"Gao","year":"2025","journal-title":"IET Intell. Transp. Syst."},{"key":"10.1016\/j.displa.2026.103487_b34","series-title":"Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","volume":"Vol. 18","author":"Ronneberger","year":"2015"},{"issue":"6","key":"10.1016\/j.displa.2026.103487_b35","doi-asserted-by":"crossref","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","article-title":"Unet++: Redesigning skip connections to exploit multiscale features in image segmentation","volume":"39","author":"Zhou","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.displa.2026.103487_b36","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1109\/TIP.2019.2926748","article-title":"Coarse-to-fine semantic segmentation from image-level labels","volume":"29","author":"Jing","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.displa.2026.103487_b37","doi-asserted-by":"crossref","first-page":"2066","DOI":"10.1109\/TIP.2019.2941644","article-title":"Semantic image segmentation by scale-adaptive networks","volume":"29","author":"Huang","year":"2019","journal-title":"IEEE Trans. Image Process."},{"issue":"8","key":"10.1016\/j.displa.2026.103487_b38","doi-asserted-by":"crossref","first-page":"2198","DOI":"10.1007\/s11263-023-01808-8","article-title":"Instance segmentation in the dark","volume":"131","author":"Chen","year":"2023","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.displa.2026.103487_b39","series-title":"Information Processing in Medical Imaging: 27th International Conference, IPMI 2021, Virtual Event, June 28\u2013June 30, 2021, Proceedings","first-page":"715","article-title":"Is segmentation uncertainty useful?","volume":"Vol. 27","author":"Czolbe","year":"2021"},{"key":"10.1016\/j.displa.2026.103487_b40","doi-asserted-by":"crossref","DOI":"10.1109\/TCSVT.2023.3272111","article-title":"Dual-uncertainty guided cycle-consistent network for zero-shot learning","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.displa.2026.103487_b41","article-title":"Uncertainty-aware weakly supervised temporal action localization with knowledge selection","author":"Yan","year":"2025","journal-title":"Displays"},{"issue":"4","key":"10.1016\/j.displa.2026.103487_b42","doi-asserted-by":"crossref","first-page":"1106","DOI":"10.1007\/s11263-020-01395-y","article-title":"Rectifying pseudo label learning via uncertainty estimation for domain adaptive semantic segmentation","volume":"129","author":"Zheng","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.displa.2026.103487_b43","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2020.107562","article-title":"Cross-modality deep feature learning for brain tumor segmentation","volume":"110","author":"Zhang","year":"2021","journal-title":"Pattern Recognit."},{"issue":"MIDL 2020 special issue","key":"10.1016\/j.displa.2026.103487_b44","first-page":"1","article-title":"An uncertainty-driven GCN refinement strategy for organ segmentation","volume":"1","author":"Soberanis Mukul","year":"2020","journal-title":"Mach. Learn. Biomed. Imaging"},{"key":"10.1016\/j.displa.2026.103487_b45","doi-asserted-by":"crossref","unstructured":"T. Kim, H. Lee, D. Kim, Uacanet: Uncertainty augmented context attention for polyp segmentation, in: Proceedings of the 29th ACM International Conference on Multimedia, 2021, pp. 2167\u20132175.","DOI":"10.1145\/3474085.3475375"},{"key":"10.1016\/j.displa.2026.103487_b46","unstructured":"L. Chen, L. Gu, Y. Fu, When semantic segmentation meets frequency aliasing, in: 12th International Conference on Learning Representations, ICLR 2024, 2024."},{"key":"10.1016\/j.displa.2026.103487_b47","article-title":"Spatial frequency modulation for semantic segmentation","author":"Chen","year":"2025","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.displa.2026.103487_b48","article-title":"Reliable mutual distillation for medical image segmentation under imperfect annotations","author":"Fang","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.displa.2026.103487_b49","doi-asserted-by":"crossref","unstructured":"K. Joseph, S. Khan, F.S. Khan, V.N. Balasubramanian, Towards open world object detection, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 5830\u20135840.","DOI":"10.1109\/CVPR46437.2021.00577"},{"key":"10.1016\/j.displa.2026.103487_b50","doi-asserted-by":"crossref","unstructured":"L. Chen, L. Gu, D. Zheng, Y. Fu, Frequency-adaptive dilated convolution for semantic segmentation, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 3414\u20133425.","DOI":"10.1109\/CVPR52733.2024.00328"},{"key":"10.1016\/j.displa.2026.103487_b51","doi-asserted-by":"crossref","unstructured":"L. Chen, L. Gu, Y. Fu, Frequency-dynamic attention modulation for dense prediction, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2025, pp. 22620\u201322632.","DOI":"10.1109\/CVPR52734.2025.02809"},{"key":"10.1016\/j.displa.2026.103487_b52","doi-asserted-by":"crossref","unstructured":"Z. Qin, P. Zhang, F. Wu, X. Li, Fcanet: Frequency channel attention networks, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2021, pp. 783\u2013792.","DOI":"10.1109\/ICCV48922.2021.00082"},{"key":"10.1016\/j.displa.2026.103487_b53","series-title":"Marginalized CNN: Learning deep invariant representations","author":"Zhao12","year":"2017"},{"key":"10.1016\/j.displa.2026.103487_b54","doi-asserted-by":"crossref","unstructured":"Q. Hou, L. Zhang, M.-M. Cheng, J. Feng, Strip pooling: Rethinking spatial pooling for scene parsing, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 4003\u20134012.","DOI":"10.1109\/CVPR42600.2020.00406"},{"key":"10.1016\/j.displa.2026.103487_b55","series-title":"ECCV","article-title":"Encoder-decoder with atrous separable convolution for semantic image segmentation","author":"Chen","year":"2018"},{"key":"10.1016\/j.displa.2026.103487_b56","series-title":"CCNet: Criss-cross attention for semantic segmentation","author":"Huang","year":"2019"},{"key":"10.1016\/j.displa.2026.103487_b57","series-title":"CVPR","article-title":"Pyramid scene parsing network","author":"Zhao","year":"2017"},{"key":"10.1016\/j.displa.2026.103487_b58","doi-asserted-by":"crossref","unstructured":"A. Kirillov, R. Girshick, K. He, P. Doll\u00e1r, Panoptic feature pyramid networks, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 6399\u20136408.","DOI":"10.1109\/CVPR.2019.00656"},{"key":"10.1016\/j.displa.2026.103487_b59","series-title":"CVPR","article-title":"Deep high-resolution representation learning for human pose estimation","author":"Sun","year":"2019"},{"key":"10.1016\/j.displa.2026.103487_b60","doi-asserted-by":"crossref","unstructured":"A. Kirillov, Y. Wu, K. He, R. Girshick, Pointrend: Image segmentation as rendering, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 9799\u20139808.","DOI":"10.1109\/CVPR42600.2020.00982"},{"key":"10.1016\/j.displa.2026.103487_b61","doi-asserted-by":"crossref","unstructured":"M. Fan, S. Lai, J. Huang, X. Wei, Z. Chai, J. Luo, X. Wei, Rethinking BiSeNet For Real-time Semantic Segmentation, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 9716\u20139725.","DOI":"10.1109\/CVPR46437.2021.00959"},{"key":"10.1016\/j.displa.2026.103487_b62","doi-asserted-by":"crossref","unstructured":"P. Chao, C.-Y. Kao, Y.-S. Ruan, C.-H. Huang, Y.-L. Lin, Hardnet: A low memory traffic network, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2019, pp. 3552\u20133561.","DOI":"10.1109\/ICCV.2019.00365"},{"key":"10.1016\/j.displa.2026.103487_b63","first-page":"1","article-title":"Bisenet v2: Bilateral network with guided aggregation for real-time semantic segmentation","author":"Yu","year":"2021","journal-title":"Int. J. Comput. Vis."},{"issue":"3","key":"10.1016\/j.displa.2026.103487_b64","doi-asserted-by":"crossref","first-page":"2437","DOI":"10.1109\/TVT.2022.3143173","article-title":"OpenMPD: An open multimodal perception dataset for autonomous driving","volume":"71","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Veh. Technol."},{"key":"10.1016\/j.displa.2026.103487_b65","series-title":"Enet: A deep neural network architecture for real-time semantic segmentation","author":"Paszke","year":"2016"},{"key":"10.1016\/j.displa.2026.103487_b66","doi-asserted-by":"crossref","unstructured":"Y. Hou, Z. Ma, C. Liu, C.C. Loy, Learning lightweight lane detection cnns by self attention distillation, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2019, pp. 1013\u20131021.","DOI":"10.1109\/ICCV.2019.00110"},{"key":"10.1016\/j.displa.2026.103487_b67","first-page":"12077","article-title":"SegFormer: Simple and efficient design for semantic segmentation with transformers","volume":"34","author":"Xie","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"11","key":"10.1016\/j.displa.2026.103487_b68","doi-asserted-by":"crossref","first-page":"4670","DOI":"10.1109\/TITS.2019.2943777","article-title":"DLT-net: Joint detection of drivable areas, lane lines, and traffic objects","volume":"21","author":"Qian","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."}],"container-title":["Displays"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0141938226001502?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0141938226001502?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T20:14:03Z","timestamp":1777493643000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0141938226001502"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":68,"alternative-id":["S0141938226001502"],"URL":"https:\/\/doi.org\/10.1016\/j.displa.2026.103487","relation":{},"ISSN":["0141-9382"],"issn-type":[{"value":"0141-9382","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Multi-grained enhancement with boundary uncertainty loss for road and lane segmentation","name":"articletitle","label":"Article Title"},{"value":"Displays","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.displa.2026.103487","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"103487"}}