Real-Time Image Defect Detection System of Cloth Digital Printing Machine

Sun, Ning; Cao, Botao

doi:10.1155/2022/5625945

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…The mAP of this method is 77%. Sun et al [25] proposed a surface defect detection system for printing fabric based on an accelerated robust feature algorithm. The detection system is used to detect the defects of white silk, spots, and wrinkles on fabric products.…”

Section: Related Workmentioning

confidence: 99%

Study on the Sustainable Detection of Machining Surface Defects under the Influence of Environmental Interference

et al. 2023

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When detecting surface defects in the industrial cutting environment, the defects are easily contaminated and covered by many interference factors (such as chips and coolant residue) that exist on the machined surface. These interfering factors hinder the sustainable detection of surface defects. Furthermore, addressing the challenge of detecting surface defects in the presence of interference factors has proven to be a difficult problem in the current detection field. To solve this problem, a sustainable detection method for surface defects is proposed. The method is divided into two steps: one is the identification and removal of interference factors; the other is the detection of surface defects. First, a new FPN-DepResUnet model is constructed by modifying the Unet model from three aspects. The FPN-DepResUnet model is used to identify the interference factors in the image. Compared to the Unet model, the MAP of the FPN-DepResUnet model is increased by 5.77%, reaching 94.82%. The interfering factors are then removed using the RFR-net model. The RFR-net model performs point-to-point repair of interference regions. The repair process is performed by finding high-quality pixels similar to the interference region from the rest of the image. The negative effects of the interfering factors are removed by combining the FPN-DepResUnet model with the RFR-net model. On this basis, the SAM-Mask RCNN model is proposed for efficient defect detection of clean surface images. Compared with the Mask RCNN model, the MAP of the proposed SAM-Mask RCNN model increased by 2.00%, reaching 94.62%. Further, the inspection results can be fed back with a variety of surface defect information including defect types, the number of pixels in the different defect regions, and the proportion of different defect regions in the entire image. This enables predictive maintenance and control of the machined surface quality during machining.

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Section: Related Workmentioning

confidence: 99%

Study on the Sustainable Detection of Machining Surface Defects under the Influence of Environmental Interference

et al. 2023

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“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

mentioning

confidence: 99%

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”

mentioning

confidence: 99%

Retracted: Real‐Time Image Defect Detection System of Cloth Digital Printing Machine

Intelligence and Neuroscience

2023

Computational Intelligence and Neuroscience

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Efficient surface defect detection in industrial screen printing with minimized labeling effort

Krassnig,

Haselmann,

Kremnitzer

et al. 2024

Integrated Computer-Aided Engineering

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As part of the evolving Industry 4.0 landscape, machine learning-based visual inspection plays a key role in enhancing production efficiency. Screen printing, a versatile and cost-effective manufacturing technique, is widely applied in industries like electronics, textiles, and automotive. However, the production of complex multilayered designs is error-prone, resulting in a variety of defect appearances and classes. These defects can be characterized as small in relation to large sample areas and weakly pronounced. Sufficient defect visualization and robust defect detection methods are essential to address these challenges, especially considering the permitted design variability. In this work, we present a novel automatic visual inspection system for surface defect detection on decorated foil plates. Customized optical modalities, integrated into a sequential inspection procedure, enable defect visualization of production-related defect classes. The introduced patch-wise defect detection methods, designed to leverage less labeled data, prove effective for industrial defect detection, meeting the given process requirements. In this context, we propose an industry-applicable and scalable data preprocessing workflow that minimizes the overall labeling effort while maintaining high detection performance, as known in supervised settings. Moreover, the presented methods, not relying on any labeled defective training data, outperformed a state-of-the-art unsupervised anomaly detection method in terms of defect detection performance and inference speed.

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Real-Time Image Defect Detection System of Cloth Digital Printing Machine

Cited by 3 publications

References 19 publications

Study on the Sustainable Detection of Machining Surface Defects under the Influence of Environmental Interference

Study on the Sustainable Detection of Machining Surface Defects under the Influence of Environmental Interference

Retracted: Real‐Time Image Defect Detection System of Cloth Digital Printing Machine

Efficient surface defect detection in industrial screen printing with minimized labeling effort

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