Ultra Fast Deep Lane Detection with Hybrid Anchor Driven Ordinal Classification (2206.07389v1)

Abstract: Modern methods mainly regard lane detection as a problem of pixel-wise segmentation, which is struggling to address the problems of efficiency and challenging scenarios like severe occlusions and extreme lighting conditions. Inspired by human perception, the recognition of lanes under severe occlusions and extreme lighting conditions is mainly based on contextual and global information. Motivated by this observation, we propose a novel, simple, yet effective formulation aiming at ultra fast speed and the problem of challenging scenarios. Specifically, we treat the process of lane detection as an anchor-driven ordinal classification problem using global features. First, we represent lanes with sparse coordinates on a series of hybrid (row and column) anchors. With the help of the anchor-driven representation, we then reformulate the lane detection task as an ordinal classification problem to get the coordinates of lanes. Our method could significantly reduce the computational cost with the anchor-driven representation. Using the large receptive field property of the ordinal classification formulation, we could also handle challenging scenarios. Extensive experiments on four lane detection datasets show that our method could achieve state-of-the-art performance in terms of both speed and accuracy. A lightweight version could even achieve 300+ frames per second(FPS). Our code is at https://github.com/cfzd/Ultra-Fast-Lane-Detection-v2.

• The paper introduces a hybrid anchor driven ordinal classification method that significantly improves the robustness and speed of lane detection.
• It combines row and column anchors to adapt to various lane orientations, reducing localization errors in complex driving scenarios.
• Empirical results show the method achieves up to 36.3 FPS on embedded platforms, validating its real-time performance in autonomous driving.

Ultra Fast Deep Lane Detection with Hybrid Anchor Driven Ordinal Classification

The research presented in the paper focuses on enhancing the efficiency and accuracy of lane detection in autonomous driving systems. The authors propose a novel approach titled Hybrid Anchor Driven Ordinal Classification, which aims to address inherent challenges in lane detection tasks, combining the strengths of different anchor-based methodologies.

Methodology

The proposed method leverages hybrid anchors to improve the robustness and speed of lane detection. This approach systematically integrates both row and column anchors, allowing it to capture the orientation and curvature of lanes more effectively. By employing a hybrid anchor framework, the method addresses varying orientations of lanes, which are often problematic for traditional single-anchor systems.

The methodology involves two key components:

1. Hybrid Anchor Representation: This element allows the system to dynamically adapt to both vertical and horizontal lane formations, offering a comprehensive detection coverage. It is particularly adept at modeling lanes that deviate significantly from standard vertical or horizontal alignments.
2. Anchor-Driven Ordinal Classification: The system employs ordinal classification techniques to refine lane detection results, ensuring a more precise localization even under complex road conditions.

Results

The paper provides an extensive evaluation of the proposed method across various embedded platforms, including CPUs and Nvidia TX2 chips. The system achieves impressive real-time or near-real-time performance under different resolutions. For instance, the method attains 36.3 FPS on NVIDIA Jetson TX2 at lower resolutions, demonstrating its applicability to real-world, resource-constrained environments.

The experimental results showcase the superiority of the hybrid anchor approach over conventional methods. The adaptation of different anchor types significantly reduces localization errors, particularly those associated with lane angle variance. This improvement is validated by the statistically derived error metrics and subsequent testing.

Implications

Practically, the hybrid anchor strategy facilitates better handling of complex road geometries in autonomous driving applications, offering enhanced resilience against variations in lane orientation and curvature. The pipeline's ability to operate effectively within embedded systems accelerates the deployment feasibility in automotive contexts.

Theoretically, integrating multiple anchor approaches within a single framework opens avenues for further research in adaptive methods for spatial pattern recognition. It challenges the existing paradigms of fixed anchor systems, advocating for more flexible, situation-aware mechanisms in computer vision tasks.

Future Directions

Future research might explore expanding the hybrid anchor framework to accommodate additional visual cues and modalities, such as integrating road signs or markings into the detection process. Building on the current system’s foundations, enhancing multi-lane detection capabilities and improving intersection treatment are potential avenues. Furthermore, optimizing the system for emerging hardware architectures could unlock higher efficiency and broader applicability.

In summary, this paper provides a substantial contribution to the lane detection landscape, proposing a system that rises to the challenges of dynamic automotive scenarios. The adoption of hybrid anchors alongside ordinal classification sets a precedent for future work in autonomous vehicle perception systems.