CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending (2007.12147v1)

Abstract: We address the curve lane detection problem which poses more realistic challenges than conventional lane detection for better facilitating modern assisted/autonomous driving systems. Current hand-designed lane detection methods are not robust enough to capture the curve lanes especially the remote parts due to the lack of modeling both long-range contextual information and detailed curve trajectory. In this paper, we propose a novel lane-sensitive architecture search framework named CurveLane-NAS to automatically capture both long-ranged coherent and accurate short-range curve information while unifying both architecture search and post-processing on curve lane predictions via point blending. It consists of three search modules: a) a feature fusion search module to find a better fusion of the local and global context for multi-level hierarchy features; b) an elastic backbone search module to explore an efficient feature extractor with good semantics and latency; c) an adaptive point blending module to search a multi-level post-processing refinement strategy to combine multi-scale head prediction. The unified framework ensures lane-sensitive predictions by the mutual guidance between NAS and adaptive point blending. Furthermore, we also steer forward to release a more challenging benchmark named CurveLanes for addressing the most difficult curve lanes. It consists of 150K images with 680K labels.The new dataset can be downloaded at github.com/xbjxh/CurveLanes (already anonymized for this submission). Experiments on the new CurveLanes show that the SOTA lane detection methods suffer substantial performance drop while our model can still reach an 80+% F1-score. Extensive experiments on traditional lane benchmarks such as CULane also demonstrate the superiority of our CurveLane-NAS, e.g. achieving a new SOTA 74.8% F1-score on CULane.

• The paper introduces CurveLane-NAS, a novel framework using neural architecture search (NAS) and adaptive point blending to improve accuracy and robustness for curve lane detection.
• CurveLane-NAS includes search modules for feature fusion and elastic backbone design, plus an adaptive point blending module for refining predictions, particularly for distant lanes.
• The authors release the large-scale CurveLanes dataset focusing on challenging curve lanes and demonstrate that CurveLane-NAS achieves significantly higher F1-scores compared to state-of-the-art methods on this new benchmark.

CurveLane-NAS: A Comprehensive Approach to Curve Lane Detection

In the ambitious pursuit of advancing lane detection capabilities, especially for curved lanes which present pronounced challenges compared to conventional straight lanes, the paper "CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending" introduces a novel framework designated as CurveLane-NAS. This method aims to improve the robustness and accuracy of lane detection by leveraging a neural architecture search (NAS) approach tailored for curve lane detection, pertinent for both traditional and autonomous driving systems.

Key Contributions

The authors make significant contributions to the field with their multi-faceted approach:

1. Unified Architecture Search Framework: The proposed CurveLane-NAS framework encompasses three search modules designed to improve the capture of lane information:
   • Feature Fusion Search Module: Enhances the integration of local and global contexts across hierarchical features to better fuse information for lane detection.
   • Elastic Backbone Search Module: Explores efficient feature extractors by adjusting channel sizing and network depth, optimizing semantic extraction and latency.
   • Adaptive Point Blending Module: Develops a multi-Level post-processing refinement strategy that amalgamates secondary head predictions for robust handling of shape variances and the detection of remote lanes.
2. Introduction of CurveLanes Benchmark: A new large-scale dataset, CurveLanes, comprising 150,000 images and 680,000 annotations, is introduced to the community. The dataset predominantly features challenging curve lanes, addressing the prevalent lack of such data in existing benchmarks.
3. Performance Metrics: In evaluations against state-of-the-art (SOTA) methods on both existing datasets like CULane and the newly introduced CurveLanes, CurveLane-NAS demonstrated substantial performance advantages. For instance, it achieves an F1-score exceeding 80% on the CurveLanes dataset, indicating a marked improvement over existing approaches that experienced significant performance declines on the same dataset.

Theoretical and Practical Implications

The research underscores several theoretical and practical implications:

• Enhanced Model Adaptability: By employing NAS, the research presents methods dynamically adaptable to task-specific requirements, showing potential across varied input scenarios. This adaptability is key for the broad deployment of lane detection technologies across different road environments.
• Scalable Benchmarking: The CurveLanes dataset adds a comprehensive range of real-world scenarios to the benchmarking landscape, serving as a realistic proving ground for future lane detection models.
• Practical Application in Autonomous Systems: Improved curve lane detection directly contributes to advancements in vehicle trajectory planning and autonomous navigation, addressing the intricate challenges of driving safety in complex road environments.

Future Directions

The paper suggests several avenues for future exploration:

• Optimizing Search Algorithms: Enhancing the efficiency and precision of NAS algorithms to further reduce computational costs while maintaining or improving accuracy.
• Integration with Larger Sensor Arrays: Combining vision-based lane detection with other sensory inputs to boost real-time detection reliability under a broader spectrum of scenarios.
• Expanded Dataset Diversity: Incorporating more varied urban and rural scenarios into datasets like CurveLanes, extending applicability and performance assessment across all potential driving conditions.

In summary, CurveLane-NAS represents a significant step forward in the specific challenge of curve lane detection. While complementing the growing demands of autonomous and assisted driving technologies, it also establishes a robust framework and dataset for continued research and development in this domain.