Citation: | TIAN Sheng, ZHANG Jianfeng, ZHANG Yutian, XU Kai. Lane Detection Algorithm Based on Dilated Convolution Pyramid Network[J]. Journal of Southwest Jiaotong University, 2020, 55(2): 386-392, 416. doi: 10.3969/j.issn.0258-2724.20181026 |
STERNLUND S, STRANDROTH J, RIZZI M, et al. The effectiveness of lane departure warning systems−a reduction in real-world passenger car injury crashes[J]. Traffic Injury Prevention, 2018, 18(2): 225-229.
|
钱基德,陈斌,钱基业,等. 基于感兴趣区域模型的车道线快速检测算法[J]. 电子科技大学学报,2018,47(3): 356-361. doi: 10.3969/j.issn.1001-0548.2018.03.006
QIAN Jide, CHEN Bin, QIAN Jiye, et al. Fast lane detection algorithm based on region of interest model[J]. Journal of University of Electronic Science and Technology of China, 2018, 47(3): 356-361. doi: 10.3969/j.issn.1001-0548.2018.03.006
|
王旭宸,卢欣辰,张恒胜,等. 一种基于平行坐标系的车道线检测算法[J]. 电子科技大学学报,2018,47(3): 362-367. doi: 10.3969/j.issn.1001-0548.2018.03.007
WANG Xuhuan, LU Xinchen, ZHANG Hengsheng, et al. A lane detection method based on parallel coordinate system[J]. Journal of University of Electronic Science and Technology of China, 2018, 47(3): 362-367. doi: 10.3969/j.issn.1001-0548.2018.03.007
|
SON J, YOO H, KIM S, et al. Real-time illumination invariant lane detection for lane departure warning system[J]. Expert Systems with Applications, 2015, 42(4): 1816-1824. doi: 10.1016/j.eswa.2014.10.024
|
NEVEN D, DE BRABANDERE B, GEORGOULIS S, et al. Towards end-to-end lane detection: an instance segmentation approach[C]//Proceedings of the 2018 IEEE Intelligent Vehicles Symposium. Suzhou: IEEE, 2018: 286-291.
|
徐国晟,张伟伟,吴训成. 基于卷积神经网络的车道线语义分割算法[J]. 电子测量与仪器学报,2018,32(7): 89-94.
XU Guosheng, ZHANG Weiwei, WU Xuncheng. Laneline semantic segmentation algorithm based on convolutional neural network[J]. Journal of Electronic Measurement and Instrumentation, 2018, 32(7): 89-94.
|
GURGHIAN A, KODURI T, BAILUR S V, et al. Deeplanes: end-to-end lane position estimation using deep neural networks[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Las Vegas: IEEE, 2016: 38-45.
|
NAROTE S P, BHUJBAL P N, NAROTE A S, et al. A review of recent advances in lane detection and departure warning system[J]. Pattern Recognition, 2018, 73: 216-234. doi: 10.1016/j.patcog.2017.08.014
|
HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 770-778.
|
ZHOU B, ZHAO H, PUIG X, et al. Semantic understanding of scenes through the ADE20K dataset[J]. International Journal of Computer Vision, 2019, 127: 302-321. doi: 10.1007/s11263-018-1140-0
|
YU F, KOLTUN V. Multi-scale context aggregation by dilated convolutions[C]//Proceedings of the 4th International Conference on Learning Representations. Puerto Rico: ICLR, 2016: 1-13.
|
CHEN L C, PAPANDREOU G, KOKKINOS I, et al. DeepLab:semantic image segmentation with deep convolutional nets,atrous convolution,and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(4): 834-848. doi: 10.1109/TPAMI.2017.2699184
|
PAN X, SHI J, LUO P, et al. Spatial as deep: spatial CNN for traffic scene understanding[C]//Proceedings of the Artificial Intelligence. New Orleans: AAAI. 2018: 7276-7283.
|
REDMON J, FARHADI A. YOLO9000: better, faster, stronger[C]//Proceedings of the Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 7263-7271.
|
GIRSHICK R. Fast R-CNN[C]//Proceedings of the Computer Vision. Santiago: IEEE, 2015: 1440-1448.
|