CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending

• URL: http://arxiv.org/abs/2007.12147v1
• Date: Thu, 23 Jul 2020 17:23:26 GMT
• Title: CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending
• Authors: Hang Xu, Shaoju Wang, Xinyue Cai, Wei Zhang, Xiaodan Liang, Zhenguo Li
• Abstract summary: CurveLane-NAS is a novel lane-sensitive architecture search framework. It captures both long-ranged coherent and accurate short-range curve information. It unifies both architecture search and post-processing on curve lane predictions via point blending.
• Score: 102.98909328368481
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Monocular Lane Detection Based on Deep Learning: A Survey [51.19079381823076]
  Lane detection plays an important role in autonomous driving perception systems. As deep learning algorithms gain popularity, monocular lane detection methods based on deep learning have demonstrated superior performance. This paper presents a comprehensive overview of existing methods, encompassing both the increasingly mature 2D lane detection approaches and the developing 3D lane detection works.
  arXiv  Detail & Related papers  (2024-11-25T12:09:43Z)
• 3D Lane Detection from Front or Surround-View using Joint-Modeling & Matching [27.588395086563978]
  We propose a joint modeling approach that combines Bezier curves and methods. We also introduce a novel 3D Spatial, representing an exploration of 3D surround-view lane detection research. This innovative method establishes a new benchmark in front-view 3D lane detection on the Openlane dataset.
  arXiv  Detail & Related papers  (2024-01-16T01:12:24Z)
• Decoupling the Curve Modeling and Pavement Regression for Lane Detection [67.22629246312283]
  curve-based lane representation is a popular approach in many lane detection methods. We propose a new approach to the lane detection task by decomposing it into two parts: curve modeling and ground height regression.
  arXiv  Detail & Related papers  (2023-09-19T11:24:14Z)
• BSNet: Lane Detection via Draw B-spline Curves Nearby [21.40607319558899]
  We revisit the curve-based lane detection methods from the perspectives of the lane representations' globality and locality. We design a simple yet efficient network BSNet to ensure the acquisition of global and local features. The proposed methods achieve state-of-the-art performance on the Tusimple, CULane, and LLAMAS datasets.
  arXiv  Detail & Related papers  (2023-01-17T14:25:40Z)
• RCLane: Relay Chain Prediction for Lane Detection [76.62424079494285]
  We present a new method for lane detection based on relay chain prediction. Our strategy allows us to establish new state-of-the-art on four major benchmarks including TuSimple, CULane, CurveLanes and LLAMAS.
  arXiv  Detail & Related papers  (2022-07-19T16:48:39Z)
• A Keypoint-based Global Association Network for Lane Detection [47.93323407661912]
  Lane detection is a challenging task that requires predicting complex topology shapes of lane lines and distinguishing different types of lanes simultaneously. We propose a Global Association Network (GANet) to formulate the lane detection problem from a new perspective. Our method outperforms previous methods with F1 score of 79.63% on CULane and 97.71% on Tusimple dataset with high FPS.
  arXiv  Detail & Related papers  (2022-04-15T05:24:04Z)
• Lane Detection with Versatile AtrousFormer and Local Semantic Guidance [92.83267435275802]
  Lane detection is one of the core functions in autonomous driving. Most existing methods tend to resort to CNN-based techniques. We propose Atrous Transformer (AtrousFormer) to solve the problem.
  arXiv  Detail & Related papers  (2022-03-08T13:25:35Z)
• Rethinking Efficient Lane Detection via Curve Modeling [37.45243848960598]
  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 CU datasets, while retaining both low latency (> 150 FPS) and small model size ( 10M)
  arXiv  Detail & Related papers  (2022-03-04T17:00:33Z)
• CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution [39.62595444015094]
  We propose CondLaneNet, a novel top-to-down lane detection framework. We also introduce a conditional lane detection strategy based on conditional convolution and row-wise formulation. Our method achieves state-of-the-art performance on three benchmark datasets.
  arXiv  Detail & Related papers  (2021-05-11T13:10:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.