Angle aided circle detection based on randomized Hough transform and its application in welding spots detection

Qiao, Liang; Long, Jianyong; Nan, Yang; Coppola, Gianmarc; Zou, Kun; Zhang, Dan; Sun, Wei

doi:10.3934/mbe.2019060

Cited by 24 publications

(15 citation statements)

References 20 publications

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“…Compared with the traditional Hough transform, the algorithm is robust, suitable for multicircle detection under complex conditions and has strong antiinterference ability. In addition, the algorithm has been successfully applied to the inspection of car body solder joints, and the experimental results verify the effectiveness and accuracy of the algorithm [7]. López and Cuevas studied a circle detection method based on the recently proposed meta-heuristic technology: an optimization algorithm based on teaching, which is a group-based technology inspired by the teaching process.…”

Section: Introductionmentioning

confidence: 91%

Detection Method of Compound Chemical Material Hole Based on Deep Learning Image

Chen

Yang

2022

Journal of Nanomaterials

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As the degree of industrialization is getting higher and higher, the requirements for the accuracy of materials are getting higher and higher. Among them, the detection of round holes in materials is particularly important. Round hole inspection is one of the important methods for material forming and precision inspection. This paper studies the round hole detection method of composite chemical materials and aims at using deep learning image technology to provide an efficient and convenient detection method for round hole detection. This paper proposes a fast circular hole detection algorithm based on contour extraction and validity judgment. The algorithm can extract the circular holes on the material sufficiently and quickly, and the image recognition technology based on deep learning can effectively improve the accuracy and efficiency of circular hole detection. Whether it is in circular contour extraction, validity analysis, or parameter calculation, the improved algorithm has shown good results. The experimental results show that the improved algorithm is significantly better than the canny algorithm for the extraction of circular hole contours. In terms of effectiveness, the calculation time of the improved algorithm is lower than the original algorithm in different data sets, and the highest is 1.14 seconds lower than the original algorithm. The error in parameter calculation is also the lowest, and the error of a set of data is as low as 0.1%.

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Section: Introductionmentioning

confidence: 91%

Detection Method of Compound Chemical Material Hole Based on Deep Learning Image

Chen

Yang

2022

Journal of Nanomaterials

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“…The larger A ( a , b , r ) is, the more likely there is a circle with center ( a , b ) and radius r . 30,31…”

Section: Detection Of Different Components In the Tire Cross-sectionmentioning

confidence: 99%

Image detection and parameterization for different components in cross-sections of radial tires

Liu

Dong

Wei

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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The internal structure analysis of radial tires is of great significance to improve vehicle safety and during tire research. In order to perform the digital analysis and detection of the internal composition in radial tire cross-sections, a detection method based on digital image processing was proposed. The research was carried out as follows: (a) the distribution detection and parametric analysis of the bead wire, steel belt, and carcass in the tire section were performed by means of digital image processing, connected domain extraction, and Hough transform; (b) using the angle of location distribution and area relationship, the detection data were optimized through coordinate and quantity relationship constraints; (c) a detection system for tire cross-section components was designed using the MATLAB platform. Our experimental results showed that this method displayed a good detection performance, and important practical significance for the research and manufacture of tires.

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“…One new example of the Hough transform is the recognition of angle aided circles and the welding spot detection on the automobile body. The method proposed in [6] utilized a novel algorithm to reduce the computational complexity of the traditional Randomized Hough transform, and reached more than 98% accuracy on a dataset of 37 welding spot test images.…”

Section: Related Workmentioning

confidence: 99%

Circular Hough Transform to improve viable Saccharomyces cerevisiae identification

Borges¹,

Silva

Quinta³

et al. 2021

Anais Do XVII Workshop De Visão Computacional (WVC 2021)

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Yeast counting is an important step in monitoring the fermentation process in sugarcane mills to optimize ethanol production. There is a need for a faster method to count viable cells in place of the fastidious and operator-dependent traditional method. In this paper, the application of a slightly modified version of the standard Circular Hough Transforms to automate the inoculated fermentation process of Saccharomyces cerevisae is reported. The results of several experiments with different preprocessing algorithms and parameter adjustments are presented. The resulting system will be part of a microbiological control procedure that is being developed to respond to Brazilian ethanol sugarcane mill's demands.

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Angle aided circle detection based on randomized Hough transform and its application in welding spots detection

Cited by 24 publications

References 20 publications

Detection Method of Compound Chemical Material Hole Based on Deep Learning Image

Detection Method of Compound Chemical Material Hole Based on Deep Learning Image

Image detection and parameterization for different components in cross-sections of radial tires

Circular Hough Transform to improve viable Saccharomyces cerevisiae identification

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