The DOI matches its Crossref record; 59 of the 59 references it cites were found in a registry or an index.
Line-CNN: End-to-End Traffic Line Detection With Line Proposal Unit
Abstract
The task of traffic line detection is a fundamental yet challenging problem. Previous approaches usually conduct traffic line detection via a two-stage way, namely the line segment detection followed by a segment clustering, which is very likely to ignore the global semantic information of an entire line. To address the problem, we propose an end-to-end system called Line-CNN (L-CNN), in which the key component is a novel line proposal unit (LPU). The LPU utilizes line proposals as references to locate accurate traffic curves, which forces the system to learn the global feature representation of the entire traffic lines. We benchmark the proposed L-CNN on two public datasets including MIKKI and TuSimple, and the results suggest that L-CNN outperforms the state-of-the-art methods. In addition, L-CNN can run at approximately 30 f/s on a Titan X GPU, which indicates the practicability and effectiveness of L-CNN for real-time intelligent self-driving systems.
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Deep Residual Learning for Image Recognition
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ImageNet classification with deep convolutional neural networks
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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You Only Look Once: Unified, Real-Time Object Detection
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Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
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(2016) · Cited by 31,590
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Self-citationLATR: 3D Lane Detection from Monocular Images with Transformer
Luo, Y., Zheng, C., Yan, X., Kun, T., Zheng, C., Cui, S., & Li, Z. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00730 · Cited by 64
Metadata
- Authors
- Li, X., Li, J., Hu, X., & Yang, J.
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2020-01
- Volume
- 21
- Issue
- 1
- Pages
- 248-258
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W2913960518
- Cited by
- 334
- References
- 59
- FWCI
- 9.84
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Line-CNN: End-to-End Traffic Line Detection With Line Proposal Unit. https://app.cochat.ai/papers/10.1109/tits.2019.2890870
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- Li, X., Li, J., Hu, X., & Yang, J. (2020). Line-CNN: End-to-End Traffic Line Detection With Line Proposal Unit. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/tits.2019.2890870
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