PolyLaneNet: Lane Estimation via Deep Polynomial Regression

By Lucas Tabelini, Rodrigo Berriel, Thiago Meireles Paixão, Claudine Santos Badue, A. D. Souza, Thiago Oliveira-Santos. International Conference on Pattern Recognition, 2020

377 citations recorded by the source corpus.

One of the main factors that contributed to the large advances in autonomous driving is the advent of deep learning. For safer self-driving vehicles, one of the problems that has yet to be solved completely is lane detection. Since methods for this task have to work in real-time (+30 FPS), they not only have to be effective (i.e., have high accuracy) but they also have to be efficient (i.e., fast). In this work, we present a novel method for lane detection that uses as input an image from a forward-looking camera mounted in the vehicle and outputs polynomials representing each lane marking in the image, via deep polynomial regression. The proposed method is shown to be competitive with existing state-of-the-art methods in the TuSimple dataset while maintaining its efficiency (115 FPS). Additionally, extensive qualitative results on two additional public datasets are presented, alongside with limitations in the evaluation metrics used by recent works for lane detection. Finally, we provide source code and trained models that allow others to replicate all the results shown in this paper, which is surprisingly rare in state-of-the-art lane detection methods.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/101baf17a9eeb89fc48391d6f8a6c273ecfb7fd4

How researchers see this paper

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

In Automotive Engineering

1. Xiaohao Chen
2. Ping Tan
3. Lizhe Liu
4. Siyu Zhu

In Computer Vision and Pattern Recognition

1. Deng Cai
2. Dacheng Tao
3. Xiaofei He
4. Ping Tan
5. Luc Van Gool
6. Ming-Hsuan Yang
7. Shuicheng Yan
8. Ming-Ming Cheng

Citations in the corpus

• CLRNet: Cross Layer Refinement Network for Lane Detection (2022, methodology): “Our method outperforms PolyLaneNet [25] and LaneATT [24] by 7.”
• End-to-end Lane Shape Prediction with Transformers (2020, methodology): “In the following section, we treat PolyLaneNet [18] as the baseline method since they also predict parametric output for lanes and provide amazingly reproducible codes and baseline models.”
• CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution (2021, methodology): “Recently, various methods such as anchor-based methods [32, 2, 39], rowwise detection methods [30, 29, 41], and parametric prediction methods [31, 25] have been proposed and continue to refresh the accuracy and efficiency.”