Review of image segmentation techniques for layup defect detection in the Automated Fiber Placement process

Meister, Sebastian; Wermes, Mahdieu A. M.; Stüve, Jan; Groves, Roger M.

doi:10.1007/s10845-021-01774-3

Cited by 36 publications

(6 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…But there are few studies that directly improve the accuracy of segmented images [ 14 ]. And the existing segmentation accuracy has been unable to meet the needs of modern society [ 15 ]. For this reason, the research innovatively proposes the MCMC image segmentation algorithm based on MRF model.…”

Section: Related Workmentioning

confidence: 99%

MCMC algorithm based on Markov random field in image segmentation

Wang,

2024

PLoS ONE

View full text Add to dashboard Cite

In the realm of digital image applications, image processing technology occupies a pivotal position, with image segmentation serving as a foundational component. As the digital image application domain expands across industries, the conventional segmentation techniques increasingly challenge to cater to modern demands. To address this gap, this paper introduces an MCMC-based image segmentation algorithm based on the Markov Random Field (MRF) model, marking a significant stride in the field. The novelty of this research lies in its method that capitalizes on domain information in pixel space, amplifying the local segmentation precision of image segmentation algorithms. Further innovation is manifested in the development of an adaptive segmentation image denoising algorithm based on MCMC sampling. This algorithm not only elevates image segmentation outcomes, but also proficiently denoises the image. In the experimental results, MRF-MCMC achieves better segmentation performance, with an average segmentation accuracy of 94.26% in Lena images, significantly superior to other common image segmentation algorithms. In addition, the study proposes that the denoising model outperforms other algorithms in peak signal-to-noise ratio and structural similarity in environments with noise standard deviations of 15, 25, and 50. In essence, these experimental findings affirm the efficacy of this study, opening avenues for refining digital image segmentation methodologies.

show abstract

Section: Related Workmentioning

confidence: 99%

MCMC algorithm based on Markov random field in image segmentation

Wang,

2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Some related studies in the field of deep learning have addressed AFP defect recognition [43]. Carsten Schmidt et al [44] proposed a defect detection and classification method based on thermal imaging and deep learning in the automatic fiber placement (AFP) process.…”

Section: Defect Detection In Afpmentioning

confidence: 99%

Research on Defect Detection in Automated Fiber Placement Processes Based on a Multi-Scale Detector

et al. 2022

View full text Add to dashboard Cite

Various surface defects in automated fiber placement (AFP) processes affect the forming quality of the components. In addition, defect detection usually requires manual observation with the naked eye, which leads to low production efficiency. Therefore, automatic solutions for defect recognition have high economic potential. In this paper, we propose a multi-scale AFP defect detection algorithm, named the spatial pyramid feature fusion YOLOv5 with channel attention (SPFFY-CA). The spatial pyramid feature fusion YOLOv5 (SPFFY) adopts spatial pyramid dilated convolutions (SPDCs) to fuse the feature maps extracted in different receptive fields, thus integrating multi-scale defect information. For the feature maps obtained from a concatenate function, channel attention (CA) can improve the representation ability of the network and generate more effective features. In addition, the sparsity training and pruning (STP) method is utilized to achieve network slimming, thus ensuring the efficiency and accuracy of defect detection. The experimental results of the PASCAL VOC and our AFP defect datasets demonstrate the effectiveness of our scheme, which achieves superior performance.

show abstract

“…The textile industry continues to grapple with the crucial challenge of determining the optimal parameters for both the raw material and the manufacturing process of synthetic fibers, particularly texturized yarn, to offer top-notch products while also minimizing production costs. Fortunately, data mining and machine learning techniques have emerged as invaluable tools in resolving this predicament (Bourdeau-Laferrière et al ., 2021; Zhang and Yang, 2014; Ogulata et al ., 2006; Ertuğrul and Aytaç, 2009; Meister et al ., 2021).…”

Section: Introductionmentioning

confidence: 99%

Developing a data-driven operational guide for the texturized yarn production process: data mining and intelligence approach

Sareminia,

Ghayoumian,

Haghighat

2024

IJCST

View full text Add to dashboard Cite

PurposeThe textile industry holds immense significance in the economy of any nation, particularly in the production of synthetic yarn and fabrics. Consequently, the pursuit of acquiring high-quality products at a reduced cost has become a significant concern for countries. The primary objective of this research is to leverage data mining and data intelligence techniques to enhance and refine the production process of texturized yarn by developing an intelligent operating guide that enables the adjustment of production process parameters in the texturized yarn manufacturing process, based on the specifications of raw materials.Design/methodology/approachThis research undertook a systematic literature review to explore the various factors that influence yarn quality. Data mining techniques, including deep learning, K-nearest neighbor (KNN), decision tree, Naïve Bayes, support vector machine and VOTE, were employed to identify the most crucial factors. Subsequently, an executive and dynamic guide was developed utilizing data intelligence tools such as Power BI (Business Intelligence). The proposed model was then applied to the production process of a textile company in Iran 2020 to 2021.FindingsThe results of this research highlight that the production process parameters exert a more significant influence on texturized yarn quality than the characteristics of raw materials. The executive production guide was designed by selecting the optimal combination of production process parameters, namely draw ratio, D/Y and primary temperature, with the incorporation of limiting indexes derived from the raw material characteristics to predict tenacity and elongation.Originality/valueThis paper contributes by introducing a novel method for creating a dynamic guide. An intelligent and dynamic guide for tenacity and elongation in texturized yarn production was proposed, boasting an approximate accuracy rate of 80%. This developed guide is dynamic and seamlessly integrated with the production database. It undergoes regular updates every three months, incorporating the selected features of the process and raw materials, their respective thresholds, and the predicted levels of elongation and tenacity.

show abstract

Review of image segmentation techniques for layup defect detection in the Automated Fiber Placement process

Cited by 36 publications

References 50 publications

MCMC algorithm based on Markov random field in image segmentation

MCMC algorithm based on Markov random field in image segmentation

Research on Defect Detection in Automated Fiber Placement Processes Based on a Multi-Scale Detector

Developing a data-driven operational guide for the texturized yarn production process: data mining and intelligence approach

Contact Info

Product

Resources

About