Advancements in Road Lane Mapping: Comparative Fine-Tuning Analysis of Deep Learning-based Semantic Segmentation Methods Using Aerial Imagery

• URL: http://arxiv.org/abs/2410.05717v2
• Date: Tue, 15 Oct 2024 17:37:45 GMT
• Title: Advancements in Road Lane Mapping: Comparative Fine-Tuning Analysis of Deep Learning-based Semantic Segmentation Methods Using Aerial Imagery
• Authors: Willow Liu, Shuxin Qiao, Kyle Gao, Hongjie He, Michael A. Chapman, Linlin Xu, Jonathan Li,
• Abstract summary: This research addresses the need for high-definition (HD) maps for autonomous vehicles (AVs) Earth observation data offers valuable resources for map creation, but specialized models for road lane extraction are still underdeveloped in remote sensing. In this study, we compare twelve foundational deep learning-based semantic segmentation models for road lane marking extraction from high-definition remote sensing images.
• Score: 16.522544814241495
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This research addresses the need for high-definition (HD) maps for autonomous vehicles (AVs), focusing on road lane information derived from aerial imagery. While Earth observation data offers valuable resources for map creation, specialized models for road lane extraction are still underdeveloped in remote sensing. In this study, we perform an extensive comparison of twelve foundational deep learning-based semantic segmentation models for road lane marking extraction from high-definition remote sensing images, assessing their performance under transfer learning with partially labeled datasets. These models were fine-tuned on the partially labeled Waterloo Urban Scene dataset, and pre-trained on the SkyScapes dataset, simulating a likely scenario of real-life model deployment under partial labeling. We observed and assessed the fine-tuning performance and overall performance. Models showed significant performance improvements after fine-tuning, with mean IoU scores ranging from 33.56% to 76.11%, and recall ranging from 66.0% to 98.96%. Transformer-based models outperformed convolutional neural networks, emphasizing the importance of model pre-training and fine-tuning in enhancing HD map development for AV navigation.

Related papers

• Guiding Attention in End-to-End Driving Models [49.762868784033785]
  Vision-based end-to-end driving models trained by imitation learning can lead to affordable solutions for autonomous driving. We study how to guide the attention of these models to improve their driving quality by adding a loss term during training. In contrast to previous work, our method does not require these salient semantic maps to be available during testing time.
  arXiv  Detail & Related papers  (2024-04-30T23:18:51Z)
• Pre-training on Synthetic Driving Data for Trajectory Prediction [61.520225216107306]
  We propose a pipeline-level solution to mitigate the issue of data scarcity in trajectory forecasting. We adopt HD map augmentation and trajectory synthesis for generating driving data, and then we learn representations by pre-training on them. We conduct extensive experiments to demonstrate the effectiveness of our data expansion and pre-training strategies.
  arXiv  Detail & Related papers  (2023-09-18T19:49:22Z)
• Video Killed the HD-Map: Predicting Multi-Agent Behavior Directly From Aerial Images [14.689298253430568]
  We propose an aerial image-based map (AIM) representation that requires minimal annotation and provides rich road context information for traffic agents like pedestrians and vehicles. Our results demonstrate competitive multi-agent trajectory prediction performance especially for pedestrians in the scene when using our AIM representation.
  arXiv  Detail & Related papers  (2023-05-19T17:48:01Z)
• PreTraM: Self-Supervised Pre-training via Connecting Trajectory and Map [58.53373202647576]
  We propose PreTraM, a self-supervised pre-training scheme for trajectory forecasting. It consists of two parts: 1) Trajectory-Map Contrastive Learning, where we project trajectories and maps to a shared embedding space with cross-modal contrastive learning, and 2) Map Contrastive Learning, where we enhance map representation with contrastive learning on large quantities of HD-maps. On top of popular baselines such as AgentFormer and Trajectron++, PreTraM boosts their performance by 5.5% and 6.9% relatively in FDE-10 on the challenging nuScenes dataset.
  arXiv  Detail & Related papers  (2022-04-21T23:01:21Z)
• Spatial-Temporal Map Vehicle Trajectory Detection Using Dynamic Mode Decomposition and Res-UNet+ Neural Networks [0.0]
  This paper presents a machine-learning-enhanced longitudinal scanline method to extract vehicle trajectories from high-angle traffic cameras. The Dynamic Mode Decomposition (DMD) method is applied to extract vehicle strands by decomposing the Spatial-Temporal Map (STMap) into the sparse foreground and low-rank background. A deep neural network named Res-UNet+ was designed for the semantic segmentation task by adapting two prevalent deep learning architectures.
  arXiv  Detail & Related papers  (2022-01-13T00:49:24Z)
• Self-supervised Audiovisual Representation Learning for Remote Sensing Data [96.23611272637943]
  We propose a self-supervised approach for pre-training deep neural networks in remote sensing. By exploiting the correspondence between geo-tagged audio recordings and remote sensing, this is done in a completely label-free manner. We show that our approach outperforms existing pre-training strategies for remote sensing imagery.
  arXiv  Detail & Related papers  (2021-08-02T07:50:50Z)
• Generating Synthetic Training Data for Deep Learning-Based UAV Trajectory Prediction [11.241614693184323]
  We present an approach for generating synthetic trajectory data of unmanned-aerial-vehicles (UAVs) in image space. We show that an RNN-based prediction model solely trained on the generated data can outperform classic reference models on a real-world UAV tracking dataset.
  arXiv  Detail & Related papers  (2021-07-01T13:08:31Z)
• GANav: Group-wise Attention Network for Classifying Navigable Regions in Unstructured Outdoor Environments [54.21959527308051]
  We present a new learning-based method for identifying safe and navigable regions in off-road terrains and unstructured environments from RGB images. Our approach consists of classifying groups of terrain classes based on their navigability levels using coarse-grained semantic segmentation. We show through extensive evaluations on the RUGD and RELLIS-3D datasets that our learning algorithm improves the accuracy of visual perception in off-road terrains for navigation.
  arXiv  Detail & Related papers  (2021-03-07T02:16:24Z)
• TrajGAIL: Generating Urban Vehicle Trajectories using Generative Adversarial Imitation Learning [9.01310450044549]
  This research suggests a generative modeling approach to learn the underlying distributions of urban vehicle trajectory data. A generative model for urban vehicle trajectories can better generalize from training data by learning the underlying distribution of the training data. TrajGAIL is a generative adversarial imitation learning framework for the urban vehicle trajectory generation.
  arXiv  Detail & Related papers  (2020-07-28T13:17:51Z)
• PerMO: Perceiving More at Once from a Single Image for Autonomous Driving [76.35684439949094]
  We present a novel approach to detect, segment, and reconstruct complete textured 3D models of vehicles from a single image. Our approach combines the strengths of deep learning and the elegance of traditional techniques. We have integrated these algorithms with an autonomous driving system.
  arXiv  Detail & Related papers  (2020-07-16T05:02:45Z)

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.