CLRmatchNet: Enhancing Curved Lane Detection with Deep Matching Process

• URL: http://arxiv.org/abs/2309.15204v2
• Date: Sun, 31 Mar 2024 20:59:03 GMT
• Title: CLRmatchNet: Enhancing Curved Lane Detection with Deep Matching Process
• Authors: Sapir Kontente, Roy Orfaig, Ben-Zion Bobrovsky,
• Abstract summary: Lane detection plays a crucial role in autonomous driving by providing vital data to ensure safe navigation. Modern algorithms rely on anchor-based detectors, which are then followed by a label-assignment process to categorize training detections as positive or negative instances. Our research introduces MatchNet, a deep learning sub module-based approach aimed at improving the label assignment process.
• Score: 0.6144680854063939
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Lane detection plays a crucial role in autonomous driving by providing vital data to ensure safe navigation. Modern algorithms rely on anchor-based detectors, which are then followed by a label-assignment process to categorize training detections as positive or negative instances based on learned geometric attributes. Accurate label assignment has great impact on the model performance, that is usually relying on a pre-defined classical cost function evaluating GT-prediction alignment. However, classical label assignment methods face limitations due to their reliance on predefined cost functions derived from low-dimensional models, potentially impacting their optimality. Our research introduces MatchNet, a deep learning submodule-based approach aimed at improving the label assignment process. Integrated into a state-of-the-art lane detection network such as the Cross Layer Refinement Network for Lane Detection (CLRNet), MatchNet replaces the conventional label assignment process with a submodule network. The integrated model, CLRmatchNet, surpasses CLRNet, showing substantial improvements in scenarios involving curved lanes, with remarkable improvement across all backbones of +2.8% for ResNet34, +2.3% for ResNet101, and +2.96% for DLA34. In addition, it maintains or even improves comparable results in other sections. Our method boosts the confidence level in lane detection, allowing an increase in the confidence threshold. Our code is available at: https://github.com/sapirkontente/CLRmatchNet.git

Related papers

• CLRKDNet: Speeding up Lane Detection with Knowledge Distillation [4.015241891536452]
  We introduce CLRKDNet, a streamlined model that balances detection accuracy with real-time performance. Our method reduces inference time by up to 60% while maintaining detection accuracy comparable to CLRNet.
  arXiv  Detail & Related papers  (2024-05-21T05:20:04Z)
• Bridging the Gap Between End-to-End and Two-Step Text Spotting [88.14552991115207]
  Bridging Text Spotting is a novel approach that resolves the error accumulation and suboptimal performance issues in two-step methods. We demonstrate the effectiveness of the proposed method through extensive experiments.
  arXiv  Detail & Related papers  (2024-04-06T13:14:04Z)
• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• AdaTriplet-RA: Domain Matching via Adaptive Triplet and Reinforced Attention for Unsupervised Domain Adaptation [15.905869933337101]
  Unsupervised domain adaption (UDA) is a transfer learning task where the data and annotations of the source domain are available but only have access to the unlabeled target data during training. We propose to improve the unsupervised domain adaptation task with an inter-domain sample matching scheme. We apply the widely-used and robust Triplet loss to match the inter-domain samples. To reduce the catastrophic effect of the inaccurate pseudo-labels generated during training, we propose a novel uncertainty measurement method to select reliable pseudo-labels automatically and progressively refine them.
  arXiv  Detail & Related papers  (2022-11-16T13:04:24Z)
• Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object Detection [85.11649974840758]
  3D object detection networks tend to be biased towards the data they are trained on. We propose a single-frame approach for source-free, unsupervised domain adaptation of lidar-based 3D object detectors.
  arXiv  Detail & Related papers  (2021-11-30T18:42:42Z)
• Learning to Rectify for Robust Learning with Noisy Labels [25.149277009932423]
  We propose warped probabilistic inference (WarPI) to achieve adaptively rectifying the training procedure for the classification network. We evaluate WarPI on four benchmarks of robust learning with noisy labels and achieve the new state-of-the-art under variant noise types.
  arXiv  Detail & Related papers  (2021-11-08T02:25:50Z)
• Towards Reducing Labeling Cost in Deep Object Detection [61.010693873330446]
  We propose a unified framework for active learning, that considers both the uncertainty and the robustness of the detector. Our method is able to pseudo-label the very confident predictions, suppressing a potential distribution drift.
  arXiv  Detail & Related papers  (2021-06-22T16:53:09Z)
• Cascade Network with Guided Loss and Hybrid Attention for Finding Good Correspondences [33.65360396430535]
  Given a putative correspondence set of an image pair, we propose a neural network which finds correct correspondences by a binary-class classifier. We propose a new Guided Loss that can directly use evaluation criterion (Fn-measure) as guidance to dynamically adjust the objective function. We then propose a hybrid attention block to extract feature, which integrates the Bayesian context normalization (BACN) and channel-wise attention (CA)
  arXiv  Detail & Related papers  (2021-01-31T08:33:20Z)
• Fully Dynamic Inference with Deep Neural Networks [19.833242253397206]
  Two compact networks, called Layer-Net (L-Net) and Channel-Net (C-Net), predict on a per-instance basis which layers or filters/channels are redundant and therefore should be skipped. On the CIFAR-10 dataset, LC-Net results in up to 11.9$times$ fewer floating-point operations (FLOPs) and up to 3.3% higher accuracy compared to other dynamic inference methods. On the ImageNet dataset, LC-Net achieves up to 1.4$times$ fewer FLOPs and up to 4.6% higher Top-1 accuracy than the other methods.
  arXiv  Detail & Related papers  (2020-07-29T23:17:48Z)
• SADet: Learning An Efficient and Accurate Pedestrian Detector [68.66857832440897]
  This paper proposes a series of systematic optimization strategies for the detection pipeline of one-stage detector. It forms a single shot anchor-based detector (SADet) for efficient and accurate pedestrian detection. Though structurally simple, it presents state-of-the-art result and real-time speed of $20$ FPS for VGA-resolution images.
  arXiv  Detail & Related papers  (2020-07-26T12:32:38Z)
• Scope Head for Accurate Localization in Object Detection [135.9979405835606]
  We propose a novel detector coined as ScopeNet, which models anchors of each location as a mutually dependent relationship. With our concise and effective design, the proposed ScopeNet achieves state-of-the-art results on COCO.
  arXiv  Detail & Related papers  (2020-05-11T04:00:09Z)

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.