用路边摄像头自动标注道路分割数据，提升恶劣天气下模型表现
Label Correction for Road Segmentation Using Road-side Cameras
- 仅需手动标注一帧，通过频域图像配准实现多帧标签迁移
- 在芬兰927个摄像头、4个月冬季数据上验证，显著提升分割性能
- 适合自动驾驶和智能交通系统，尤其适用于天气变化场景
在各种天气条件下实现可靠的车道分割对智能交通、自动驾驶和高级驾驶辅助系统至关重要。为确保鲁棒性，深度学习感知模型的训练数据需涵盖全气候条件，但收集和标注此类数据耗时耗力。本文利用现有路边摄像头基础设施，自动采集不同天气下的道路数据，并提出一种新型半自动标注方法：针对每路摄像头仅需手动标注一帧，再通过频域图像配准补偿小幅度相机移动，将标签迁移到其他帧。该方法在芬兰927个摄像头、为期4个月的冬季数据上验证。使用半自动标注数据训练后，多个深度学习分割模型的性能均得到提升。测试在两个数据集上进行：一个是在域内（路边摄像头）的数据集，另一个是域外（车载摄像头）的数据集，以评估模型鲁棒性。
原文摘要 · Abstract (English)
Reliable road segmentation in all weather conditions is critical for intelligent transportation applications, autonomous vehicles and advanced driver's assistance systems. For robust performance, all weather conditions should be included in the training data of deep learning-based perception models. However, collecting and annotating such a dataset requires extensive resources. In this paper, existing roadside camera infrastructure is utilized for collecting road data in varying weather conditions automatically. Additionally, a novel semi-automatic annotation method for roadside cameras is proposed. For each camera, only one frame is labeled manually and then the label is transferred to other frames of that camera feed. The small camera movements between frames are compensated using frequency domain image registration. The proposed method is validated with roadside camera data collected from 927 cameras across Finland over 4 month time period during winter. Training on the semi-automatically labeled data boosted the segmentation performance of several deep learning segmentation models. Testing was carried out on two different datasets to evaluate the robustness of the resulting models. These datasets were an in-domain roadside camera dataset and out-of-domain dataset captured with a vehicle on-board camera.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。