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

Bangchao Liu, Youping Chen, Jingming Xie, Bing Chen and Padmapriya Praveenkumar

Complexity, 2021, vol. 2021, 1-10

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.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:2036466

DOI: 10.1155/2021/2036466

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