Quality control automation of electric cables using machine vision

Lenty, Bartosz; Sioma, Andrzej; Kwiek, Paweł Jacek

doi:10.1117/12.2501562

Cited by 11 publications

(6 citation statements)

References 6 publications

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“…The effect of temperature on digital cameras and its adverse influence on captured images can also be observed in other applications, not only in 3D-structured light scanners. Digital cameras are often used in industrial applications to measure geometric features with high precision, up to 2 μm [ 15 , 16 ]. The temperature in the production hall can vary, and the temperature amplitude in a day–night cycle can also be very high.…”

Section: Introductionmentioning

confidence: 99%

Temperature Compensation Method for Digital Cameras in 2D and 3D Measurement Applications

Adamczyk

Liberadzki

Sitnik

2018

Sensors

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This paper presents the results of several studies concerning the effect of temperature on digital cameras. Experiments were performed using three different camera models. The presented results conclusively demonstrate that the typical camera design does not adequately take into account the effect of temperature variation on the device’s performance. In this regard, a modified camera design is proposed that exhibits a highly predictable behavior under varying ambient temperature and facilitates thermal compensation. A novel temperature compensation method is also proposed. This compensation model can be applied in almost every existing camera application, as it is compatible with every camera calibration model. A two-dimensional (2D) and three-dimensional (3D) application of the proposed compensation model is also described. The results of the application of the proposed compensation approach are presented herein.

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Section: Introductionmentioning

confidence: 99%

Temperature Compensation Method for Digital Cameras in 2D and 3D Measurement Applications

Adamczyk

Liberadzki

Sitnik

2018

Sensors

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“…Chengyu et al [ 5 ] proposed abnormal event detection system for bolts looseness detection in flange connection. Bartosz et al [ 6 ] introduced a quality control automation of electric cables, which is able to detect defects in electric cables. Chen et al summarizes data-driven fault diagnosis methods that can detect and diagnose faults for traction systems in high-speed trains automatically.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Incident Detection on Spectrometers Using Deep Convolutional Models

Zhang

Leye

et al. 2021

Sensors

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This paper focuses on improving the performance of scientific instrumentation that uses glass spray chambers for sample introduction, such as spectrometers, which are widely used in analytical chemistry, by detecting incidents using deep convolutional models. The performance of these instruments can be affected by the quality of the introduction of the sample into the spray chamber. Among the indicators of poor quality sample introduction are two primary incidents: The formation of liquid beads on the surface of the spray chamber, and flooding at the bottom of the spray chamber. Detecting such events autonomously as they occur can assist with improving the overall operational accuracy and efficacy of the chemical analysis, and avoid severe incidents such as malfunction and instrument damage. In contrast to objects commonly seen in the real world, beading and flooding detection are more challenging since they are of significantly small size and transparent. Furthermore, the non-rigid property increases the difficulty of the detection of these incidents, as such that existing deep-learning-based object detection frameworks are prone to fail for this task. There is no former work that uses computer vision to detect these incidents in the chemistry industry. In this work, we propose two frameworks for the detection task of these two incidents, which not only leverage the modern deep learning architectures but also integrate with expert knowledge of the problems. Specifically, the proposed networks first localize the regions of interest where the incidents are most likely generated and then refine these incident outputs. The use of data augmentation and synthesis, and choice of negative sampling in training, allows for a large increase in accuracy while remaining a real-time system for inference. In the data collected from our laboratory, our method surpasses widely used object detection baselines and can correctly detect 95% of the beads and 98% of the flooding. At the same time, out method can process four frames per second and is able to be implemented in real time.

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“…The paper presents examples of using 3D images in the implementation of tasks to check defects in geometry and surface defects of the product. The advantage of the method is that the three-dimensional image enables the implementation of a much larger number of measurements and analyses, especially in the field of spatial measurements, complementing and extending the currently used vision methods in the video industry based on two-dimensional image analysis [10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Automated Control of Surface Defects on Ceramic Tiles Using 3D Image Analysis

Sioma

2020

Materials

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This paper presents a method of acquisition and analysis of three-dimensional images in the task of automatic location and evaluation of defects on the surface of ceramic tiles. It presents a brief description of selected defects appearing on the surface of tiles, along with the analysis of their formation. The paper includes the presentation of the method of constructing a 3D image of the tile surface using the Laser Triangulation Method (LTM), along with the surface imaging parameters employed in the research. The algorithms of three-dimensional surface image analysis of ceramic tiles used in the process of image filtering and defect identification are presented. For selected defects, the method of measuring defect parameters and the method of visualization of defects on the surface are also presented. The developed method was tested on defective products to confirm its effectiveness in the field of quick defect detection in automated control systems installed on production lines.

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Quality control automation of electric cables using machine vision

Cited by 11 publications

References 6 publications

Temperature Compensation Method for Digital Cameras in 2D and 3D Measurement Applications

Temperature Compensation Method for Digital Cameras in 2D and 3D Measurement Applications

Autonomous Incident Detection on Spectrometers Using Deep Convolutional Models

Automated Control of Surface Defects on Ceramic Tiles Using 3D Image Analysis

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