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.

翻译：本文提出了一种基于半监督框架的图像线段检测方法。通过利用基于不同增强和扰动的未标记图像的一致性损失，并结合少量标记数据，我们展示了与全监督方法相当的结果。这为标注困难或成本高昂的应用场景以及模型的领域特定适应开辟了可能性。我们特别关注实时和在线应用，并研究了小型高效的学习骨干网络。据我们所知，我们的方法是首个利用现代最先进的半监督学习方法针对线段检测的研究。我们在标准基准测试和林业应用的领域特定场景中测试了该方法，证明了所提方法的可行性。