CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution

By Lizhe Liu, Xiaohao Chen, Siyu Zhu, Ping Tan. IEEE International Conference on Computer Vision, 2021

326 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Automotive Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/039d4956224e7fe280fe61bcbb816fbd96d5745e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Dacheng Tao
2. Yunhe Wang
3. Kai Han
4. Chunjing Xu
5. Deng Cai
6. Junjie Yan
7. Jianyuan Guo
8. Jian‐Yuan Sun

In Automotive Engineering

1. Dacheng Tao

In Computer Vision and Pattern Recognition

1. Dacheng Tao
2. Yunhe Wang
3. Luc Van Gool
4. Kai Han
5. Shuicheng Yan
6. Xiaogang Wang
7. Yi Yang
8. Jiashi Feng

Citations in the corpus

• A Survey on Vision Transformer (2020, methodology): “[143] utilized a transformer encoder structure for more efficient context feature extraction.”
• CLRNet: Cross Layer Refinement Network for Lane Detection (2022, methodology): “CondLaneNet [12] introduces a conditional lane detection strategy based on conditional convolution and row anchor-based formulation, i.e. , it first locates start points of lane lines then performs row anchor-based lane detection.”
• PETRv2: A Unified Framework for 3D Perception from Multi-Camera Images (2022, methodology): “‡ denotes projecting 2D lane results from CondLaneNet [22] to BEV using IPM.”