LSDNet: Trainable Modification of LSD Algorithm for Real-Time Line Segment Detection
As of today, the best accuracy in line segment detection (LSD) is achieved by algorithms based on convolutional neural networks – CNNs. Unfortunately, these methods utilize deep, heavy networks and are slower than traditional model-based detectors. In this paper we build an accurate yet f...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9761231/ |
| _version_ | 1818504705780744192 |
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| author | Lev Teplyakov Leonid Erlygin Evgeny Shvets |
| author_facet | Lev Teplyakov Leonid Erlygin Evgeny Shvets |
| author_sort | Lev Teplyakov |
| collection | DOAJ |
| description | As of today, the best accuracy in line segment detection (LSD) is achieved by algorithms based on convolutional neural networks – CNNs. Unfortunately, these methods utilize deep, heavy networks and are slower than traditional model-based detectors. In this paper we build an accurate yet fast CNN-based detector, LSDNet, by incorporating a lightweight CNN into a classical LSD detector. Specifically, we replace the first step of the original LSD algorithm – construction of line segments heatmap and tangent field from raw image gradients – with a lightweight CNN, which is able to calculate more complex and rich features. The second part of the LSD algorithm is used with only minor modifications. Compared with several modern line segment detectors on standard Wireframe dataset, the proposed LSDNet provides the highest speed (among CNN-based detectors) of 214 FPS with a competitive accuracy of <inline-formula> <tex-math notation="LaTeX">$78~F^{H}$ </tex-math></inline-formula>. Although the best-reported accuracy is <inline-formula> <tex-math notation="LaTeX">$83~F^{H}$ </tex-math></inline-formula> at 33 FPS, we speculate that the observed accuracy gap is caused by errors in annotations and the actual gap is significantly lower. We point out systematic inconsistencies in the annotations of popular line detection benchmarks – Wireframe and York Urban, carefully reannotate a subset of images and show that (i) existing detectors have improved quality on updated annotations without retraining, suggesting that new annotations correlate better with the notion of correct line segment detection; (ii) the gap between accuracies of our detector and others diminishes to negligible <inline-formula> <tex-math notation="LaTeX">$0.2~F^{H}$ </tex-math></inline-formula>, with our method being the fastest. |
| first_indexed | 2024-12-10T21:40:49Z |
| format | Article |
| id | doaj.art-4c71f09f09cf47d09740c7a19e997c26 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-10T21:40:49Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4c71f09f09cf47d09740c7a19e997c262022-12-22T01:32:30ZengIEEEIEEE Access2169-35362022-01-0110452564526510.1109/ACCESS.2022.31691779761231LSDNet: Trainable Modification of LSD Algorithm for Real-Time Line Segment DetectionLev Teplyakov0https://orcid.org/0000-0003-2720-8795Leonid Erlygin1Evgeny Shvets2Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, RussiaInstitute for Information Transmission Problems, Russian Academy of Sciences, Moscow, RussiaInstitute for Information Transmission Problems, Russian Academy of Sciences, Moscow, RussiaAs of today, the best accuracy in line segment detection (LSD) is achieved by algorithms based on convolutional neural networks – CNNs. Unfortunately, these methods utilize deep, heavy networks and are slower than traditional model-based detectors. In this paper we build an accurate yet fast CNN-based detector, LSDNet, by incorporating a lightweight CNN into a classical LSD detector. Specifically, we replace the first step of the original LSD algorithm – construction of line segments heatmap and tangent field from raw image gradients – with a lightweight CNN, which is able to calculate more complex and rich features. The second part of the LSD algorithm is used with only minor modifications. Compared with several modern line segment detectors on standard Wireframe dataset, the proposed LSDNet provides the highest speed (among CNN-based detectors) of 214 FPS with a competitive accuracy of <inline-formula> <tex-math notation="LaTeX">$78~F^{H}$ </tex-math></inline-formula>. Although the best-reported accuracy is <inline-formula> <tex-math notation="LaTeX">$83~F^{H}$ </tex-math></inline-formula> at 33 FPS, we speculate that the observed accuracy gap is caused by errors in annotations and the actual gap is significantly lower. We point out systematic inconsistencies in the annotations of popular line detection benchmarks – Wireframe and York Urban, carefully reannotate a subset of images and show that (i) existing detectors have improved quality on updated annotations without retraining, suggesting that new annotations correlate better with the notion of correct line segment detection; (ii) the gap between accuracies of our detector and others diminishes to negligible <inline-formula> <tex-math notation="LaTeX">$0.2~F^{H}$ </tex-math></inline-formula>, with our method being the fastest.https://ieeexplore.ieee.org/document/9761231/Convolutional neural networksedge detectionline segment detectionU-netLSD |
| spellingShingle | Lev Teplyakov Leonid Erlygin Evgeny Shvets LSDNet: Trainable Modification of LSD Algorithm for Real-Time Line Segment Detection IEEE Access Convolutional neural networks edge detection line segment detection U-net LSD |
| title | LSDNet: Trainable Modification of LSD Algorithm for Real-Time Line Segment Detection |
| title_full | LSDNet: Trainable Modification of LSD Algorithm for Real-Time Line Segment Detection |
| title_fullStr | LSDNet: Trainable Modification of LSD Algorithm for Real-Time Line Segment Detection |
| title_full_unstemmed | LSDNet: Trainable Modification of LSD Algorithm for Real-Time Line Segment Detection |
| title_short | LSDNet: Trainable Modification of LSD Algorithm for Real-Time Line Segment Detection |
| title_sort | lsdnet trainable modification of lsd algorithm for real time line segment detection |
| topic | Convolutional neural networks edge detection line segment detection U-net LSD |
| url | https://ieeexplore.ieee.org/document/9761231/ |
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