Abstract
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.
Highlights
Many defect detection methods have been proposed for roll-to-roll printing process, and these methods can effectively detect a variety of printing defects in real time
In order to eliminate the interferences as much as possible, we propose a multi-template edge feature fusion algorithm to increase the accuracy of defect identification. e architecture of the proposed method model is shown in Figure 7, and the formula of feature fusion is expressed as follows: FTemplate F1|F2|. . . |Fn, (6)
To demonstrate the effectiveness of the method proposed in this paper, a comparison is made for the other detection methods including non-fusion registration difference and convolutional neural network (CNN). e result is shown in Table 5. e method proposed in this paper achieves an accuracy of 93.09% which outperforms other methods. e reason for this result is that the low-quality images are not suitable for the traditional differential detection algorithm
Summary
Many defect detection methods have been proposed for roll-to-roll printing process, and these methods can effectively detect a variety of printing defects in real time. Paper [28] adopted a single convolutional neural network (CNN) model that can extract effective features for defect classification without using additional feature extraction algorithms, and the proposed method can identify defect classes not seen during training by comparing the CNN features of the unseen classes with those of the trained classes. Paper [29] proposed a vision-based method using a deep architecture of convolutional neural networks (CNNs) for detecting concrete cracks without calculating the defect features. Traditional defect detection methods based on image difference cannot be applied to low-quality images effectively and it is difficult to get enough defect samples to train a machine learning model for defect identification.
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