Papers › DT-LSD: Deformable Transformer-based Line Segment Detection
DT-LSD: Deformable Transformer-based Line Segment Detection
Sebastian Janampa, Marios Pattichis
Line segment detection is a fundamental low-level task in computer vision, and improvements in this task can impact more advanced methods that depend on it. Most new methods developed for line segment detection are based on Convolutional Neural Networks (CNNs). Our paper seeks to address challenges that prevent the wider adoption of transformer-based methods for line segment detection. More specifically, we introduce a new model called Deformable Transformer-based Line Segment Detection (DT-LSD) that supports cross-scale interactions and can be trained quickly. This work proposes a novel Deformable Transformer-based Line Segment Detector (DT-LSD) that addresses LETR's drawbacks. For faster training, we introduce Line Contrastive DeNoising (LCDN), a technique that stabilizes the one-to-one matching process and speeds up training by 34×. We show that DT-LSD is faster and more accurate than its predecessor transformer-based model (LETR) and outperforms all CNN-based models in terms of accuracy. In the Wireframe dataset, DT-LSD achieves 71.7 for sAP¹⁰ and 73.9 for sAP¹⁵; while 33.2 for sAP¹⁰ and 35.1 for sAP¹⁵ in the YorkUrban dataset.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Line Segment Detection | York Urban Dataset | DT-LSD | sAP10 | 33.2 | #3 of 16 | Archive leaderboard | report |
| Line Segment Detection | York Urban Dataset | DT-LSD | sAP15 | 35.1 | #3 of 16 | Archive leaderboard | report |
| Line Segment Detection | York Urban Dataset | DT-LSD | sAP5 | 30.2 | #3 of 16 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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