The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution
Open accessAbstract
Modern deep-learning-based lane detection methods are successful in most scenarios but struggling for lane lines with complex topologies. In this work, we propose CondLaneNet, a novel top-to-down lane detection framework that detects the lane instances first and then dynamically predicts the line shape for each instance. Aiming to resolve lane instance-level discrimination problem, we introduce a conditional lane detection strategy based on conditional convolution and row-wise formulation. Further, we design the Recurrent Instance Module(RIM) to overcome the problem of detecting lane lines with complex topologies such as dense lines and fork lines. Benefit from the end-to-end pipeline which requires little post-process, our method has real-time efficiency. We extensively evaluate our method on three benchmarks of lane detection. Results show that our method achieves state-of-the-art performance on all three benchmark datasets. Moreover, our method has the coexistence of accuracy and efficiency, e.g. a 78.14 F1 score and 220 FPS on CULane. Our code is available at https://github.com/aliyun/conditional-lane-detection.
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Feature Pyramid Networks for Object Detection
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PETRv2: A Unified Framework for 3D Perception from Multi-Camera Images
Liu, Y., Yan, J., Jia, F., Li, S., Gao, A., Wang, T., & Zhang, X. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00302 · Cited by 384
LATR: 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
- Liu, L., Chen, X., Zhu, S., & Tan, P.
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 3753-3762
- OpenAlex
- W3203848864
- Cited by
- 346
- References
- 39
- FWCI
- 57.78
- Type
- conference paper
- Language
- en
- Open access
- green
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- Liu, L., Chen, X., Zhu, S., & Tan, P. (2021). CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv48922.2021.00375
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