The Impact of Semi-supervised Learning on Line Segment Detection
Engman, Johanna LU ; Åström, Kalle LU
and Oskarsson, Magnus
LU
(2025)
23rd Scandinavian Conference on Image Analysis, SCIA 2025
In Lecture Notes in Computer Science
15726 LNCS.
p.123-136
- Abstract
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... (More)
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.
(Less)
- author
- Engman, Johanna
LU
; Åström, Kalle
LU
and Oskarsson, Magnus
LU
- organization
- publishing date
- 2025
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Domain adaptation, Line detection, Semi-supervised learning
- host publication
- Image Analysis - 23rd Scandinavian Conference, SCIA 2025, Proceedings
- series title
- Lecture Notes in Computer Science
- editor
- Petersen, Jens and Dahl, Vedrana Andersen
- volume
- 15726 LNCS
- pages
- 14 pages
- publisher
- Springer Science and Business Media B.V.
- conference name
- 23rd Scandinavian Conference on Image Analysis, SCIA 2025
- conference location
- Reykjavik, Iceland
- conference dates
- 2025-06-23 - 2025-06-25
- external identifiers
-
- scopus:105009757869
- ISSN
- 1611-3349
- 0302-9743
- ISBN
- 9783031959172
- DOI
- 10.1007/978-3-031-95918-9_9
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
- id
- f6f1cd43-0b8d-488b-b1e2-69edebaa6694
- date added to LUP
- 2025-11-07 10:49:13
- date last changed
- 2026-02-17 10:36:12
@inproceedings{f6f1cd43-0b8d-488b-b1e2-69edebaa6694,
abstract = {{<p>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.</p>}},
author = {{Engman, Johanna and Åström, Kalle and Oskarsson, Magnus}},
booktitle = {{Image Analysis - 23rd Scandinavian Conference, SCIA 2025, Proceedings}},
editor = {{Petersen, Jens and Dahl, Vedrana Andersen}},
isbn = {{9783031959172}},
issn = {{1611-3349}},
keywords = {{Domain adaptation; Line detection; Semi-supervised learning}},
language = {{eng}},
pages = {{123--136}},
publisher = {{Springer Science and Business Media B.V.}},
series = {{Lecture Notes in Computer Science}},
title = {{The Impact of Semi-supervised Learning on Line Segment Detection}},
url = {{http://dx.doi.org/10.1007/978-3-031-95918-9_9}},
doi = {{10.1007/978-3-031-95918-9_9}},
volume = {{15726 LNCS}},
year = {{2025}},
}