Rethinking Efficient Lane Detection via Curve Modeling (2203.02431v2)

Abstract: This paper presents a novel parametric curve-based method for lane detection in RGB images. Unlike state-of-the-art segmentation-based and point detection-based methods that typically require heuristics to either decode predictions or formulate a large sum of anchors, the curve-based methods can learn holistic lane representations naturally. To handle the optimization difficulties of existing polynomial curve methods, we propose to exploit the parametric B\'ezier curve due to its ease of computation, stability, and high freedom degrees of transformations. In addition, we propose the deformable convolution-based feature flip fusion, for exploiting the symmetry properties of lanes in driving scenes. The proposed method achieves a new state-of-the-art performance on the popular LLAMAS benchmark. It also achieves favorable accuracy on the TuSimple and CULane datasets, while retaining both low latency (> 150 FPS) and small model size (< 10M). Our method can serve as a new baseline, to shed the light on the parametric curves modeling for lane detection. Codes of our model and PytorchAutoDrive: a unified framework for self-driving perception, are available at: https://github.com/voldemortX/pytorch-auto-drive .

• The paper proposes a novel parametric curve-based method using Bézier curves to streamline lane detection and reduce reliance on heuristic post-processing.
• It introduces a deformable convolution-based feature flip fusion technique that leverages lane symmetry for enhanced feature aggregation.
• The approach delivers real-time performance (>150 FPS) with a compact model size (<10M parameters), making it highly suitable for autonomous driving applications.

Overview of "Rethinking Efficient Lane Detection via Curve Modeling"

The paper "Rethinking Efficient Lane Detection via Curve Modeling" presents a parametric curve-based approach to lane detection, focusing on the application of Bézier curves for this task. Traditional methods often rely on segmentation-based or point detection approaches, which come with a set of challenges such as the need for heuristic post-processing and large anchor sets. The authors propose the use of Bézier curves to achieve a more holistic and efficient representation of lane lines in RGB images.

Methodology

The motivation for utilizing Bézier curves stems from their computational simplicity, stability, and adaptability in capturing the geometric transformations common in lane detection scenarios. By leveraging the parametric properties of Bézier curves, the proposed method aims to address the optimization difficulties associated with polynomial curves in previous approaches.

The researchers introduce a novel deformable convolution-based feature flip fusion technique to utilize the symmetry of lane lines in driving scenes. This contributes to capturing global structures by aggregating features with horizontally flipped versions, thus reinforcing lane symmetry. The technique aligns with the characteristic symmetries observed in driving environments, enhancing the robustness of the model.

Results

The paper yields impressive results on prominent lane detection benchmarks such as LLAMAS, TuSimple, and CULane. Notably, the method sets a new performance benchmark on the LLAMAS dataset. Key metrics include retaining high accuracy with low latency (>150 FPS) and maintaining a compact model size (<10M parameters).

Implications

Practically, this method can serve as a new baseline for lane detection research using parametric curve modeling. Its efficiency and low computational cost make it particularly attractive for real-time applications in autonomous driving technologies. Theoretically, it could influence the broader application of parametric curves in different areas of computer vision that require geometric transformations.

Future Directions

Potential future developments could explore deeper integration of parametric curve models into other self-driving components, such as path planning or vehicle control systems. Further, expanding the model capability to handle more complex driving conditions, such as varying weather or lighting scenarios, could enhance its utility.

Overall, this research advances the field of lane detection, providing a more streamlined and robust alternative to traditional methods by effectively leveraging the properties of Bézier curves.