An Intelligent Online Presswork Defect Detection Method and System

Xiang-qian, Peng; Chen, Youping; Xie, Jingming; Liu, Huaiguang; Gu, Chenlin

doi:10.1109/itcs.2010.45

Cited by 3 publications

(2 citation statements)

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“…Compared with the traditional method to extract the gradient edge of gray image, the proposed edge detection algorithm has better detection performance, and it can reduce the information loss of RGB three channels and make the edge extraction more accurate. Paper [10] provided an image fusion method, which used multi-channel image subtraction to segment defects. e method can update the reference image continuously with the printing production.…”

Section: Related Researchmentioning

confidence: 99%

Industrial Printing Image Defect Detection Using Multi-Edge Feature Fusion Algorithm

et al. 2021

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Online defect detection system is a necessary technical measure and important means for large-scale industrial printing production. It is effective to reduce artificial detection fatigue and improve the accuracy and stability of industry printing line. However, the existing defect detection algorithms are mainly developed based on high-quality database and it is difficult to detect the defects on low-quality printing images. In this paper, we propose a new multi-edge feature fusion algorithm which is effective in solving this problem. Firstly, according to the characteristics of sheet-fed printing system, a new printing image database is established; compared with the existing databases, it has larger translation, deformation, and uneven illumination variation. These interferences make defect detection become more challenging. Then, SIFT feature is employed to register the database. In order to reduce the number of false detections which are caused by the position, deformation, and brightness deviation between the detected image and reference image, multi-edge feature fusion algorithm is proposed to overcome the effects of these disturbances. Lastly, the experimental results of mAP (92.65%) and recall (96.29%) verify the effectiveness of the proposed method which can effectively detect defects in low-quality printing database. The proposed research results can improve the adaptability of visual inspection system on a variety of different printing platforms. It is better to control the printing process and further reduce the number of operators.

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Section: Related Researchmentioning

confidence: 99%

Industrial Printing Image Defect Detection Using Multi-Edge Feature Fusion Algorithm

et al. 2021

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“…Kim et al 52 developed a new steel strip surface inspection system using K-nearest neighbor –based image processing. Peng et al 53 proposed a real-time presswork defect detection technique based on machine vision. For a stable and high-speed inspection, defect segments are detected based on multi-channel image subtraction.…”

Section: Introductionmentioning

confidence: 99%

Rapid and non-invasive surface crack detection for pressed-panel products based on online image processing

Jung

Park

2019

Structural Health Monitoring

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Crack detection during the manufacturing process of pressed-panel products is an important aspect of quality management. Traditional approaches for crack detection of those products are subjective and expensive because they are usually performed by experienced human inspectors. Therefore, the development and implementation of an automated and accurate inspection system is required for the manufacturing process. In this article, a crack detection technique based on image processing is proposed that utilizes the images of panel products captured by a regular camera system. First, the binary panel object image is extracted from various backgrounds after considering the color factor. Edge lines are then generated from a binary image using a percolation process. Finally, crack detection and localization is performed with a unique edge-line evaluation. In order to demonstrate the capability of the proposed technique, lab-scale experiments were carried out with a thin aluminum plate. In addition, a test was performed with the panel images acquired at an actual press line. Experimental results show that the proposed technique could effectively detect panel cracks at an improved rate and speed. The experimental results also demonstrate that the proposed technique could be an extension of structural health monitoring frameworks into a new manufacturing application.

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PPR-Net: Patch-Based Multi-scale Pyramid Registration Network for Defect Detection of Printed Label

et al. 2023

Computer Vision – ACCV 2022

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An Intelligent Online Presswork Defect Detection Method and System

Cited by 3 publications

References 2 publications

Industrial Printing Image Defect Detection Using Multi-Edge Feature Fusion Algorithm

Industrial Printing Image Defect Detection Using Multi-Edge Feature Fusion Algorithm

Rapid and non-invasive surface crack detection for pressed-panel products based on online image processing

PPR-Net: Patch-Based Multi-scale Pyramid Registration Network for Defect Detection of Printed Label

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