Towards Light-Weight and Real-Time Line Segment Detection

By Geonmo Gu, Byungsoo Ko, SeoungHyun Go, Sung-Hyun Lee, Jingeun Lee, Minchul Shin. 2021

99 citations recorded by the source corpus.

Previous deep learning-based line segment detection (LSD) suffers from the immense model size and high computational cost for line prediction. This constrains them from real-time inference on computationally restricted environments. In this paper, we propose a real-time and light-weight line segment detector for resource-constrained environments named Mobile LSD (M-LSD). We design an extremely efficient LSD architecture by minimizing the backbone network and removing the typical multi-module process for line prediction found in previous methods. To maintain competitive performance with a light-weight network, we present novel training schemes: Segments of Line segment (SoL) augmentation, matching and geometric loss. SoL augmentation splits a line segment into multiple subparts, which are used to provide auxiliary line data during the training process. Moreover, the matching and geometric loss allow a model to capture additional geometric cues. Compared with TP-LSD-Lite, previously the best real-time LSD method, our model (M-LSD-tiny) achieves competitive performance with 2.5% of model size and an increase of 130.5% in inference speed on GPU. Furthermore, our model runs at 56.8 FPS and 48.6 FPS on the latest Android and iPhone mobile devices, respectively. To the best of our knowledge, this is the first real-time deep LSD available on mobile devices.

Subfields: Aerospace Engineering, Civil and Structural Engineering, Computer Vision and Pattern Recognition, Efficiency & Compression

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Citations in the corpus

Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models (2023, neutral): “Remove Objects from Image [10, 35] Replace Objects from Image [10, 35] Change Image by the Text [35] Image Question Answering [23] Image-to-Text [23] Text-to-Image [35] Image-to-Edge [45] Edge-to-Image [53] Image-to-Line [16] Line-to-Image [53] Image-to-Hed [44] Hed-to-Image [53] Image-to-Seg [24] S”
Uni-ControlNet: All-in-One Control to Text-to-Image Diffusion Models (2023, methodology): “In this paper, we consider seven example local conditions, including Canny edge [42], MLSD edge [43], HED boundary [44], sketch [45, 46], Openpose [47], Midas depth [48], and segmentation mask [49].”
Adding Conditional Control to Text-to-Image Diffusion Models (2023, methodology): “Normal map Depth map Canny[11] edge M-LSD[24] line HED[90] edge ADE20k[95]seg.”