用少量标注数据实现高精度线段检测，适合标注困难场景。
The Impact of Semi-Supervised Learning on Line Segment Detection
- 基于一致性损失，利用少量标注与大量未标注图像训练
- 在标准数据集上达到与全监督方法相当的检测精度
- 适用于实时、在线的林业等特定领域应用
本文提出一种基于半监督框架的图像线段检测方法。通过在少量标注数据基础上，利用不同增强和扰动的未标注图像设计一致性损失，实现了与全监督方法相当的性能。该方法为标注成本高或难以获取的场景提供了可行方案，并支持模型在特定领域的适应性优化。研究聚焦于实时与在线应用，采用小型高效的学习骨干网络。据我们所知，这是首个采用现代先进半监督学习方法进行线段检测的工作。我们在标准基准和林业应用的特定场景中进行了测试，验证了方法的有效性与可部署性。
原文摘要 · Abstract (English)
In this paper we present a method for line segment detection in images, based on a semi-supervised framework. Leveraging the use of a consistency loss based on differently augmented and perturbed unlabeled images with a small amount of labeled data, we show comparable results to fully supervised methods. This opens up application scenarios where annotation is difficult or expensive, and for domain specific adaptation of models. We are specifically interested in real-time and online applications, and investigate small and efficient learning backbones. Our method is to our knowledge the first to target line detection using modern state-of-the-art methodologies for semi-supervised learning. We test the method on both standard benchmarks and domain specific scenarios for forestry applications, showing the tractability of the proposed method.
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