The Impact of Semi-Supervised Learning on Line Segment Detection
This work addresses the problem of reducing annotation costs for line detection in images, particularly for domain-specific applications like forestry, though it is incremental as it applies existing semi-supervised techniques to a new task.
The paper tackles line segment detection in images by proposing a semi-supervised learning method that uses a consistency loss with unlabeled data and minimal labeled data, achieving results comparable to fully supervised approaches and demonstrating applicability in forestry and real-time scenarios.
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.