DT-LSD: Deformable Transformer-Based Line Segment Detection

Sebastian Janampa, Marios Pattichis; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 3477-3486

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 34X. 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 sAP^10 and 73.9 for sAP^15; while 33.2 for sAP^10 and 35.1 for sAP^15 in the YorkUrban dataset. Code available at https://github.com/SebastianJanampa/DT-LSD.