Towards Light-Weight and Real-Time Line Segment Detection

Gu, Geonmo; Ko, Byungsoo; Go, SeoungHyun; Lee, Sung-Hyun; Lee, Jingeun; Shin, Min-Chul

doi:10.1609/aaai.v36i1.19953

Cited by 31 publications

(12 citation statements)

References 32 publications

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“…As indicated in Table 2, a comparative analysis was conducted between the proposed method in this study and other models including LSD, 23 DWP, AFM, 24 LCNN, TP-LSD, 16 M-LSD, 25 and FE-LSD 26 . The ones marked in bold in the table represent the algorithm that performs best in this metric.…”

Section: Experiments and Analysismentioning

confidence: 99%

Rapid feature detection on multi-surface of metal parts based on six-dimensional pose estimation from a single RGB image

Ye,

Luo,

Zeng

et al. 2024

J. Electron. Imag.

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Feature detection on multi-surface textureless metal parts is a common and crucial task in smart manufacturing. Traditional visual detection methods typically detect only one surface at a time, leading to inefficiency. In this work, we propose a framework for the rapid detection of multi-surface features on metal parts based on a non-parallel imaging method. The framework is built upon the principles of wireframe modeling and feature modeling, combining a lightweight neural network with a traditional template matching method. First, the line segment profile features of metal parts are extracted based on a lightweight neural network. The part is then represented by the correlation descriptors of the line segments. The initial bitmap of the part is obtained through template matching, followed by obtaining the accurate six-dimensional (6D) bitmap of the metal part through iterative matching of endpoints. Subsequently, utilizing the 6D pose information, the visible surface of the part undergoes transformation through orthographic projection, and the conformity of the transformed surface is assessed. Finally, experimental validation was conducted using a hydraulic valve as the object. The experimental results indicated that our method accurately estimates the bit-pose of textureless images. Moreover, it can concurrently perform visual detection tasks on multiple visible surfaces by employing a straightforward orthographic projection transformation.

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Section: Experiments and Analysismentioning

confidence: 99%

Rapid feature detection on multi-surface of metal parts based on six-dimensional pose estimation from a single RGB image

Ye,

Luo,

Zeng

et al. 2024

J. Electron. Imag.

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show abstract

“…Line detection method. (a) Line parameterization by Hough transform; (b) line segment detection by traditional deep neural network (DNN)‐based method, i.e., Mobile line segment detection (Gu et al., 2022); (c) semantic line detection by deep Hough transform (DHT) in this paper; (d) DHT.…”

Section: Crack Segmentation and Weld Line Detection On The 2d Imagementioning

confidence: 99%

Vision‐based fatigue crack automatic perception and geometric updating of finite element model for welded joint in steel structures

Gao,

Yuanzhou,

et al. 2024

Computer aided Civil Eng

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Digital twin requires establishing a self‐updated model to simulate the structural damage perceived onsite. Despite the great success in damage identification and quantification, the difficulty in registration still limits the efficiency of model updating. This study presented a framework that enables a finite element (FE) model of welded joints to remesh itself for updating the geometric changes caused by the fatigue crack. Leveraging the linear geometry of the weld, a crack registration algorithm was proposed for the automation of crack perception. First, a dual‐task network was established to identify the crack and weld on the 2D image, where the deep Hough transform was introduced to detect the positioning weld among the irregular structural geometry. With the time‐of‐flight technique, the crack was then reconstructed and quantified in the 3D camera coordinate system. Meanwhile, the 3D structure coordinate system was established from positioning welds. Through simple coordinate transformation, the fatigue crack was automatically registered to the welded joint. Finally, the perception algorithms were integrated with the FE model, taking about 1 min to map the crack into the model. Under laboratory tests, the perception performance was not sensitive to the camera pose. The perceived errors were mainly reflected in the crack local morphology, not leading to improper reconstruction of the structural stiffness matrix.

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“…Therefore, the projected line segment of the jib on the image is selected as the geometric feature

, where

is the length of the projected line and

is the angle of the projected line segment to the

-axis. A mobile line segment detector (MLSD) [ 68 ] is applied to extract straight lines in this paper. The specific process is shown in Figure 8 , which is divided into two steps: contour extraction and line segment detection based on MLSD.…”

Section: Framework and Designmentioning

confidence: 99%

Vision-Based Automated Recognition and 3D Localization Framework for Tower Cranes Using Far-Field Cameras

Wang

Zhang

Yang

et al. 2023

Sensors

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Tower cranes can cover most of the area of a construction site, which brings significant safety risks, including potential collisions with other entities. To address these issues, it is necessary to obtain accurate and real-time information on the orientation and location of tower cranes and hooks. As a non-invasive sensing method, computer vision-based (CVB) technology is widely applied on construction sites for object detection and three-dimensional (3D) localization. However, most existing methods mainly address the localization on the construction ground plane or rely on specific viewpoints and positions. To address these issues, this study proposes a framework for the real-time recognition and localization of tower cranes and hooks using monocular far-field cameras. The framework consists of four steps: far-field camera autocalibration using feature matching and horizon-line detection, deep learning-based segmentation of tower cranes, geometric feature reconstruction of tower cranes, and 3D localization estimation. The pose estimation of tower cranes using monocular far-field cameras with arbitrary views is the main contribution of this paper. To evaluate the proposed framework, a series of comprehensive experiments were conducted on construction sites in different scenarios and compared with ground-truth data obtained by sensors. The experimental results show that the proposed framework achieves high precision in both crane jib orientation estimation and hook position estimation, thereby contributing to the development of safety management and productivity analysis.

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Towards Light-Weight and Real-Time Line Segment Detection

Cited by 31 publications

References 32 publications

Rapid feature detection on multi-surface of metal parts based on six-dimensional pose estimation from a single RGB image

Rapid feature detection on multi-surface of metal parts based on six-dimensional pose estimation from a single RGB image

Vision‐based fatigue crack automatic perception and geometric updating of finite element model for welded joint in steel structures

Vision-Based Automated Recognition and 3D Localization Framework for Tower Cranes Using Far-Field Cameras

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