| ZHANG Jun (张军)*,WANG Xingbin**,GUO Binglei*.[J].高技术通讯(英文),2022,28(3):307~316 |
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| LDNet: structure-focused lane detection based on line deformation |
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| DOI：10.3772/j.issn.1006-6748.2022.03.010 |
| 中文关键词: |
| 英文关键词: autonomous driving, convolutional neural networks (CNNs), lane detection, line deformation |
| 基金项目: |
| Author Name | Affiliation | | ZHANG Jun (张军)* | (*School of Computer Engineering, Hubei University of Arts and Science, Xiangyang 411053, P.R.China)
(**Institute of Information Engineering, Chinese Academy of Science, Beijing 100093, P.R.China) | | WANG Xingbin** | (*School of Computer Engineering, Hubei University of Arts and Science, Xiangyang 411053, P.R.China)
(**Institute of Information Engineering, Chinese Academy of Science, Beijing 100093, P.R.China) | | GUO Binglei* | (*School of Computer Engineering, Hubei University of Arts and Science, Xiangyang 411053, P.R.China)
(**Institute of Information Engineering, Chinese Academy of Science, Beijing 100093, P.R.China) |
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| 中文摘要: |
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| 英文摘要: |
| Lane detection is a fundamental necessary task for autonomous driving. The conventional methods mainly treat lane detection as a pixel-wise segmentation problem, which suffers from the challenge of uncontrollable driving road environments and needs post-processing to abstract the lane parameters. In this work,a series of lines are used to represent traffic lanes and a novel line deformation network (LDNet) is proposed to directly predict the coordinates of lane line points. Inspired by the dynamic behavior of classic snake algorithms, LDNet uses a neural network to iteratively deform an initial lane line to match the lane markings. To capture the long and discontinuous structures of lane lines, 1D convolution in LDNet is used for structured feature learning along the lane lines. Based on LDNet, a two-stage pipeline is developed for lane marking detection: (1) initial lane line proposal to predict a list of lane line candidates, and (2) lane line deformation to obtain the coordinates of lane line points. Experiments show that the proposed approach achieves competitive performances on the TuSimple dataset while being efficient for real-time applications on a GTX 1650 GPU. In particular, the accuracy of LDNet with the annotated starting and ending points is up to 99.45%, which indicates the improved initial lane line proposal method can further enhance the performance of LDNet. |
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