FIGURE 1 - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
| Stereo line-scan camera system and imaging model. (A) Stereo line-scan camera system and (B) Imaging model of stereo line-scan cameras.
Source publication
Camera calibration plays an important role in various applications including machine vision and optical measurement. In this paper, we propose a new calibration method of stereo line-scan cameras for 2D coordinate measurement using a simple calibration target rather than requiring complex calibration patterns or an auxiliary area-scan camera. Consi...
Contexts in source publication
Context 1
... our system, the side-by-side mode is chosen. As depicted in Figure 1A, the system mainly includes two line-scan cameras (Dalsa, spL2048-140km) mounted lens (Nikon, AF Nikkor 14 mm f/2.8D), two line lasers, a industrial computer with two frame grabbers (Dalsa, OR-X4C0-XPF00), several mechanical and calibration devices. Like most stereo line-scan camera systems [2,[4][5][6][7]29], our system also requires the two linescan cameras to be coplanar aligned manually using a suitable mechanical device so that the two viewing planes determined by the projection center and the pixel elements are approximately coplanar. ...
Context 2
... is necessary for an imaging model to define the coordinate systems in order to describe the intrinsic and extrinsic parameters. As shown in Figure 1B, the world coordinate system, the two camera coordinate systems and the two pixel coordinate systems are denoted by ...
Context 4
... evaluate the effectiveness of the proposed method, we calibrate our stereo line-scan camera system. It can be observed from Figures 1A and 2A, when the calibration target is translated on the flat plate, the coordinate map can give the 2D world coordinates (X w , Y w ) for camera calibration. In our experiment, n 7, that is to say we translate target seven times, a total of 56(8 × 7) feature points. ...
Similar publications
Soccer videos are a rich playground for computer vision, involving many elements, such as players, lines, and specific objects. Hence, to capture the richness of this sport and allow for fine automated analyses, we release SoccerNet-v3, a major extension of the SoccerNet dataset, providing a wide variety of spatial annotations and cross-view corres...
Citations
... These methods aim to ascertain the undistorted position of the distorted points in the image. Methods [20][21][22][23] utilize the principle that straight lines in the scene remain straight in the image to correct the distortion. Xue [22] corrected the fisheye camera by learning the relationship between image distortion and linear curvature through a deep learning network. ...
In the human–robot collaboration system, the high-precision distortion correction of the camera as an important sensor is a crucial prerequisite for accomplishing the task. The traditional correction process is to calculate the lens distortion with the camera model parameters or separately from the camera model. However, in the optimization process calculate with the camera model parameters, the mutual compensation between the parameters may lead to numerical instability, and the existing distortion correction methods separated from the camera model are difficult to ensure the accuracy of the correction. To address this problem, this study proposes a model-independent lens distortion correction method based on the image center area from the perspective of the actual camera lens distortion principle. The proposed method is based on the idea that the structured image preserves its ratios through perspective transformation, and uses the local image information in the central area of the image to correct the overall image. The experiments are verified from two cases of low distortion and high distortion under simulation and actual experiments. The experimental results show that the accuracy and stability of this method are better than other methods in training and testing results.
... There are very few CPP solutions for line scanning robotic systems (Kapetanovic et al., 2018). Compared with area-scan sensors, a line-scanning sensor is more suitable for defect inspection in industrial/manufacturing applications due to higher spatial resolution and lower production costs (Steger & Ulrich, 2021;Wang et al., 2022). Unlike a common area camera or other optical sensors that only work at some discrete positions, a line scanner utilizes only single beam scanning light to detect 3D objects when capturing images, and it needs to move continuously using a robotics manipulator along the coverage path. ...
The automatic inspection of surface defects is an important task for quality control in the computers, communications, and consumer electronics (3C) industry. Conventional devices for defect inspection (viz. line-scan sensors) have a limited field of view, thus, a robot-aided defect inspection system needs to scan the object from multiple viewpoints. Optimally selecting the robot's viewpoints and planning a path is regarded as coverage path planning (CPP), a problem that enables inspecting the object's complete surface while reducing the scanning time and avoiding misdetection of defects. However, the development of CPP strategies for robotic line scanners has not been sufficiently studied by researchers. To fill this gap in the literature, in this paper, we present a new approach for robotic line scanners to detect surface defects of 3C free-form objects automatically. Our proposed solution consists of generating a local path by a new hybrid region segmentation method and an adaptive planning algorithm to ensure the coverage of the complete object surface. An optimization method for the global path sequence is developed to maximize the scanning efficiency. To verify our proposed methodology, we conduct detailed simulation-based and experimental studies on various free-form workpieces, and compare its performance with a state-of-the-art solution. The reported results demonstrate the feasibility and effectiveness of our approach.
The engine lining can prevent fuel from debonding with the insulating layer, isolate heat, prevent combustion and buffer stress. Although its proportion is small, the bonding performance of the lining is directly related to whether the engine can maintain a complete structure, and also determines the stability of fuel combustion. How to identify the defects of the formed lining and complete the comprehensive detection of the lining is of great significance to maintain the stability of the engine structure. In this study, an image acquisition system composed of industrial cameras, line-array lenses and line-light sources is used to achieve the integrity acquisition of the lining surface image. Then, through self-made small-scale data sets for the lining image, the backbone network design, and the improvement of existing detectors and network components, the high-precision identification of image defects is achieved.
