CLRNet: Cross Layer Refinement Network for Lane Detection

• URL: http://arxiv.org/abs/2203.10350v1
• Date: Sat, 19 Mar 2022 16:11:35 GMT
• Title: CLRNet: Cross Layer Refinement Network for Lane Detection
• Authors: Tu Zheng, Yifei Huang, Yang Liu, Wenjian Tang, Zheng Yang, Deng Cai, Xiaofei He
• Abstract summary: We present Cross Layer Refinement Network (CLRNet) aiming at fully utilizing both high-level and low-level features in lane detection. CLRNet first detects lanes with high-level semantic features then performs refinement based on low-level features. In addition to our novel network design, we introduce Line IoU loss which regresses the lane line as a whole unit to improve the localization accuracy.
• Score: 36.10035201796672
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Lane is critical in the vision navigation system of the intelligent vehicle. Naturally, lane is a traffic sign with high-level semantics, whereas it owns the specific local pattern which needs detailed low-level features to localize accurately. Using different feature levels is of great importance for accurate lane detection, but it is still under-explored. In this work, we present Cross Layer Refinement Network (CLRNet) aiming at fully utilizing both high-level and low-level features in lane detection. In particular, it first detects lanes with high-level semantic features then performs refinement based on low-level features. In this way, we can exploit more contextual information to detect lanes while leveraging local detailed lane features to improve localization accuracy. We present ROIGather to gather global context, which further enhances the feature representation of lanes. In addition to our novel network design, we introduce Line IoU loss which regresses the lane line as a whole unit to improve the localization accuracy. Experiments demonstrate that the proposed method greatly outperforms the state-of-the-art lane detection approaches.

Related papers

• Sketch and Refine: Towards Fast and Accurate Lane Detection [69.63287721343907]
  Lane detection is a challenging task due to the complexity of real-world scenarios. Existing approaches, whether proposal-based or keypoint-based, suffer from depicting lanes effectively and efficiently. We present a "Sketch-and-Refine" paradigm that utilizes the merits of both keypoint-based and proposal-based methods. Experiments show that our SRLane can run at a fast speed (i.e., 278 FPS) while yielding an F1 score of 78.9%.
  arXiv  Detail & Related papers  (2024-01-26T09:28:14Z)
• LaneSegNet: Map Learning with Lane Segment Perception for Autonomous Driving [60.55208681215818]
  We introduce LaneSegNet, the first end-to-end mapping network generating lane segments to obtain a complete representation of the road structure. Our algorithm features two key modifications. One is a lane attention module to capture pivotal region details within the long-range feature space. On the OpenLane-V2 dataset, LaneSegNet outperforms previous counterparts by a substantial gain across three tasks.
  arXiv  Detail & Related papers  (2023-12-26T16:22:10Z)
• HoughLaneNet: Lane Detection with Deep Hough Transform and Dynamic Convolution [8.97991745734826]
  Lanes can present difficulties for detection, as they can be narrow, fragmented, and often obscured by heavy traffic. We propose a hierarchical Deep Hough Transform (DHT) approach that combines all lane features in an image into the Hough parameter space. Our proposed network structure demonstrates improved performance in detecting heavily occluded or worn lane images.
  arXiv  Detail & Related papers  (2023-07-07T10:08:29Z)
• 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)
• 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)
• Global Context-Aware Progressive Aggregation Network for Salient Object Detection [117.943116761278]
  We propose a novel network named GCPANet to integrate low-level appearance features, high-level semantic features, and global context features. We show that the proposed approach outperforms the state-of-the-art methods both quantitatively and qualitatively.
  arXiv  Detail & Related papers  (2020-03-02T04:26:10Z)

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.