Keep your Eyes on the Lane: Real-time Attention-guided Lane Detection

By Lucas Tabelini, Rodrigo Berriel, Thiago Meireles Paixão, Claudine Santos Badue, A. D. Souza, Thiago Oliveira-Santos. Computer Vision and Pattern Recognition, 2020

469 citations recorded by the source corpus.

Modern lane detection methods have achieved remarkable performances in complex real-world scenarios, but many have issues maintaining real-time efficiency, which is important for autonomous vehicles. In this work, we pro-pose LaneATT: an anchor-based deep lane detection model, which, akin to other generic deep object detectors, uses the anchors for the feature pooling step. Since lanes follow a regular pattern and are highly correlated, we hypothesize that in some cases global information may be crucial to infer their positions, especially in conditions such as occlusion, missing lane markers, and others. Thus, this work proposes a novel anchor-based attention mechanism that aggregates global information. The model was evaluated extensively on three of the most widely used datasets in the literature. The results show that our method outperforms the current state-of-the-art methods showing both higher efficacy and efficiency. Moreover, an ablation study is performed along with a discussion on efficiency trade-off options that are useful in practice. Code and models are available at https://github.com/lucastabelini/LaneATT.

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

View Paper: https://www.semanticscholar.org/paper/00b0f67a932ca1d71206063cce86c6acf82b31d8

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. Deng Cai
2. Xinggang Wang
3. Xiaofei He
4. Wenyu Liu
5. Qian Zhang
6. Zheng Yang
7. Yang Liu
8. Tu Zheng

In Computer Vision and Pattern Recognition

1. Xinggang Wang
2. Deng Cai
3. Qian Zhang
4. Wenyu Liu
5. Luc Van Gool
6. Dacheng Tao
7. Xiaofei He
8. Chang Huang

Citations in the corpus

• CLRNet: Cross Layer Refinement Network for Lane Detection (2022, methodology): “Our method outperforms Poly-LaneNet [25] and LaneATT [24] Table 2.”
• MapTR: Structured Modeling and Learning for Online Vectorized HD Map Construction (2022, methodology): “LaneATT (Tabelini et al., 2021) utilizes anchor-based deep lane detection model to achieve good trade-off between accuracy and efficiency.”