Abstract
Detecting defects in printed labels is essential to ensure product quality. Reference-based comparison is a potential method to challenge this task, which is widely used for defect detection. However, this method gets poor performance under large deformation, due to the lack of ability of registering the testing image with the reference image. Therefore, accurate image registration is an urgent case for defect detection of printed labels. In this paper, a patch-based multi-scale pyramid registration network (PPR-Net) is proposed. First, an image patch splitting and stitching strategy is proposed, which is scalable in image resolution. Second, a multi-scale pyramid registration module is designed to fuse multiple convolutional features to enhance the registration capability for large deformation, which gradually refines multi-scale deformation fields in a coarse-to-fine manner. Third, a distortion loss function is introduced to improve text distortions of registered images. Finally, a synthetic database is generated based on real printed labels, to simulate defective printed labels with large deformation for performance comparison. Extensive experimental results show that our method dramatically outperforms other comparable approaches.
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Acknowledgements
This work was supported in part by NSFC fund (62176077, 62272133, 61906162), in part by the Shenzhen Colleges and Universities Stable Support Program No. GXWD20220811170100001, in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2019B1515120055, in part by the Shenzhen Key Technical Project under Grant 2020N046, in part by the Shenzhen Fundamental Research Fund under Grant JCYJ20210324132210025, in part by Shenzhen Science and Technology Program (RCBS20200714114910193).
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Li, D., Li, Y., Li, J., Lu, G. (2023). PPR-Net: Patch-Based Multi-scale Pyramid Registration Network for Defect Detection of Printed Label. In: Wang, L., Gall, J., Chin, TJ., Sato, I., Chellappa, R. (eds) Computer Vision – ACCV 2022. ACCV 2022. Lecture Notes in Computer Science, vol 13842. Springer, Cham. https://doi.org/10.1007/978-3-031-26284-5_19
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