Ultra Fast Structure-aware Deep Lane Detection (2004.11757v4)

Abstract: Modern methods mainly regard lane detection as a problem of pixel-wise segmentation, which is struggling to address the problem of challenging scenarios and speed. Inspired by human perception, the recognition of lanes under severe occlusion 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 extremely fast speed and challenging scenarios. Specifically, we treat the process of lane detection as a row-based selecting problem using global features. With the help of row-based selecting, our formulation could significantly reduce the computational cost. Using a large receptive field on global features, we could also handle the challenging scenarios. Moreover, based on the formulation, we also propose a structural loss to explicitly model the structure of lanes. Extensive experiments on two lane detection benchmark datasets show that our method could achieve the state-of-the-art performance in terms of both speed and accuracy. A light-weight version could even achieve 300+ frames per second with the same resolution, which is at least 4x faster than previous state-of-the-art methods. Our code will be made publicly available.

• The paper reformulates lane detection as a row-based selection problem to reduce computational complexity while maintaining high detection accuracy.
• The introduction of a novel structural loss captures lane continuity, improving performance under occlusion and extreme lighting conditions.
• Empirical validation on CULane and TuSimple benchmarks demonstrates state-of-the-art results with speeds over 300 FPS and accuracy of 96%.

Ultra Fast Structure-aware Deep Lane Detection

The paper "Ultra Fast Structure-aware Deep Lane Detection" presents a novel approach to the problem of lane detection in challenging scenarios with a focus on speed and structural awareness. The authors reformulate lane detection as a row-based selection problem using global features, rather than the traditional pixel-wise segmentation approach.

Paper Overview

The proposed method emphasizes computational efficiency by leveraging a row-based selecting technique. This approach significantly reduces the computational burden compared to conventional segmentation methods, which require dense pixel-wise classification. By using a large receptive field on global features, the method also addresses scenarios with severe occlusion or extreme lighting.

The authors introduce a structural loss that explicitly models lane structures to improve detection accuracy. This is particularly useful in capturing the continuity and geometric properties of lanes that are not easily encoded by pixel-level segmentation.

Key Contributions

1. Formulation of Lane Detection: The paper proposes treating lane detection as a row-based selection problem. This reduces the computational complexity and enhances speed, achieving over 300 frames per second (FPS) with a lightweight version, making it at least four times faster than previous methods.
2. Structural Loss: A novel structural loss is introduced to incorporate prior information about lane rigidity and smoothness, optimizing the relationships between selected locations on predefined rows.
3. Empirical Validation: The method is validated on two benchmark datasets, CULane and TuSimple, demonstrating state-of-the-art performance in terms of speed and accuracy. Notably, the proposed method achieves accuracy comparable to more computationally intensive approaches.

Numerical Results

The approach achieves remarkable speed without sacrificing accuracy, capturing the structure and continuity of lanes more effectively than existing methods. On the TuSimple dataset, the proposed method achieves an accuracy of 96.06%, with a throughput of up to 322.5 FPS when using a Resnet-18 backbone.

Implications and Future Work

This research presents significant implications for real-time applications, especially in autonomous driving systems where computational resources and speed are critical. The innovative use of global features and structural losses provides a pathway for future exploration in reducing computational loads while maintaining high detection accuracy.

Further research could explore adaptive lane detection that dynamically selects computational strategies based on the complexity of driving scenes. The integration of this approach with other modalities, such as LIDAR and radar, could also enhance robustness and accuracy in diverse environmental conditions.

In conclusion, the reformulation of lane detection as a structure-aware, row-based selection problem presents an effective solution to balancing speed and accuracy, setting a foundation for advancements in high-performance lane detection systems.