This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Neven D, Brabandere B D, Georgoulis S, et al. Towards End-to-End Lane Detection: an Instance Segmentation Approach [J]. IEEE, 2018.NevenDBrabandereB DGeorgoulisSTowards End-to-End Lane Detection: an Instance Segmentation Approach [J]IEEE201810.1109/IVS.2018.8500547Search in Google Scholar
Pan X, Shi J, Luo P, et al. Spatial As Deep: Spatial CNN for Traffic Scene Understanding. 2017.PanXShiJLuoPSpatial As Deep: Spatial CNN for Traffic Scene Understanding201710.1609/aaai.v32i1.12301Search in Google Scholar
Hou Y, Ma Z, Liu C, et al. Learning Lightweight Lane Detection CNNs by Self Attention Distillation [J]. 2019HouYMaZLiuCLearning Lightweight Lane Detection CNNs by Self Attention Distillation [J]201910.1109/ICCV.2019.00110Search in Google Scholar
An improved YOLOv3 model based on skipping connections and spatial pyramid pooling [J]. Systems Science & Control Engineering, 2021, 9(S1).An improved YOLOv3 model based on skipping connections and spatial pyramid pooling [J]Systems Science & Control Engineering20219S110.1080/21642583.2020.1824132Search in Google Scholar
Chun-yang CHENG, Min LI, Xue-wu ZHANG, Yu-bo XIE, Yan XIANG, Jin-bao SHENG. A Lane Detection Algorithm under Complex Scenes [A]. Advanced Science and Industry Research Center. Proceedings of 2017 2nd International Conference on Computer, Mechatronics and Electronic Engineering(CMEE 2017)[C]. Advanced Science and Industry Research Center: Science and Engineering Research Center, 2017:5.Chun-yangCHENGMinLIXue-wuZHANGYu-boXIEYanXIANGJin-baoSHENGA Lane Detection Algorithm under Complex Scenes [A]. Advanced Science and Industry Research CenterProceedings of 2017 2nd International Conference on Computer, Mechatronics and Electronic Engineering(CMEE 2017)[C]. Advanced Science and Industry Research Center: Science and Engineering Research Center2017510.12783/dtcse/cmee2017/20007Search in Google Scholar
Xu H, Wang S, Cai X, et al. CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending [J]. 2020.XuHWangSCaiXCurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending [J]202010.1007/978-3-030-58555-6_41Search in Google Scholar
Q. Zou, H. Jiang, Q. Dai, et al. Robust Lane Detection From Continuous Driving Scenes Using Deep Neural Networks [J]. 2019.Q.ZouH.JiangQ.DaiRobust Lane Detection From Continuous Driving Scenes Using Deep Neural Networks [J]201910.1109/TVT.2019.2949603Search in Google Scholar
Zhihuan Wu, Yongming Gao, Lei Li, Junshi Xue, Yuntao Li. Semantic segmentation of high-resolution remote sensing images using fully convolutional network with adaptive threshold [J]. Connection Science, 2019, 31(2).ZhihuanWuYongmingGaoLeiLiJunshiXueYuntaoLiSemantic segmentation of high-resolution remote sensing images using fully convolutional network with adaptive threshold [J]Connection Science201931210.1080/09540091.2018.1510902Search in Google Scholar
Vadim Romanuke. A Prototype Model for Semantic Segmentation of Curvilinear Meandering Regions by Deconvolutional Neural Networks [J]. Applied Computer Systems, 2020, 25(1).VadimRomanukeA Prototype Model for Semantic Segmentation of Curvilinear Meandering Regions by Deconvolutional Neural Networks [J]Applied Computer Systems202025110.2478/acss-2020-0008Search in Google Scholar
Chu He, Shenglin Li, Dehui Xiong, Peizhang Fang, Mingsheng Liao. Remote Sensing Image Semantic Segmentation Based on Edge Information Guidance [J]. Remote Sensing, 2020, 12(9).ChuHeShenglinLiDehuiXiongPeizhangFangMingshengLiaoRemote Sensing Image Semantic Segmentation Based on Edge Information Guidance [J]Remote Sensing202012910.3390/rs12091501Search in Google Scholar
Lin T Y, Dollar P, Girshick R, et al. Feature Pyramid Networks for Object Detection[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society, 2017.LinT YDollarPGirshickRFeature Pyramid Networks for Object Detection[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)IEEE Computer Society201710.1109/CVPR.2017.106Search in Google Scholar
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation[C]//CVPR. IEEE, 2014.Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation[C]//CVPRIEEE2014Search in Google Scholar
He K, Gkioxari G, P Dollár, et al. Mask R-CNN [J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017.HeKGkioxariGPDollárMask R-CNN [J]IEEE Transactions on Pattern Analysis & Machine Intelligence201710.1109/ICCV.2017.322Search in Google Scholar
Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2015.LongJShelhamerEDarrellTFully convolutional networks for semantic segmentation[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)IEEE201510.1109/CVPR.2015.7298965Search in Google Scholar
Badrinarayanan V, Kendall A, Cipolla R. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation [J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017:1-1.BadrinarayananVKendallACipollaRSegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation [J]IEEE Transactions on Pattern Analysis & Machine Intelligence20171110.1109/TPAMI.2016.264461528060704Search in Google Scholar
Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer International Publishing, 2015.RonnebergerOFischerPBroxTU-Net: Convolutional Networks for Biomedical Image Segmentation[C]//International Conference on Medical Image Computing and Computer-Assisted InterventionSpringer International Publishing201510.1007/978-3-319-24574-4_28Search in Google Scholar
Paszke A, Chaurasia A, Kim S, et al. ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation [J]. 2016.PaszkeAChaurasiaAKimSENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation [J]2016Search in Google Scholar
ZHAO H S, SHI J P, (}I X J, et al. Pyramid network[C]//Proceedings of the 2017 1EEE Conference on Computer Vision and Pattern Recognition. New York: IEEE Press, 2017:2881-2890. DOI: 10.1109/CVPR.2017.660.ZHAOH SSHIJ P(}I X J, et alPyramid network[C]//Proceedings of the 2017 1EEE Conference on Computer Vision and Pattern RecognitionNew YorkIEEE Press201728812890. DOI: 10.1109/CVPR.2017.66010.1109/CVPR.2017.660Search in Google Scholar
Romera E, Alvarez J M, Bergasa L M, et al. ERFNet: Efficient Residual Factorized ConvNet for Real-Time Semantic Segmentation [J]. IEEE Transactions on Intelligent Transportation Systems, 2017, PP(1):1-10.RomeraEAlvarezJ MBergasaL MERFNet: Efficient Residual Factorized ConvNet for Real-Time Semantic Segmentation [J]IEEE Transactions on Intelligent Transportation Systems2017, PP111010.1109/IVS.2017.7995966Search in Google Scholar
CHEN L C, ZHU Y, PAPANDREOU G, et al. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation [EB/OL]. [2018-08-09]. https://arxiv.org/pef/1802.0261v1.pdf.20.FPNCHENL CZHUYPAPANDREOUGEncoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation [EB/OL].[20180809]https://arxiv.org/pef/1802.0261v1.pdf.20.FPNSearch in Google Scholar
Lin T Y, Dollar P, Girshick R, et al. Feature Pyramid Networks for Object Detection[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society, 2017.LinT YDollarPGirshickRFeature Pyramid Networks for Object Detection[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)IEEE Computer Society201710.1109/CVPR.2017.106Search in Google Scholar