Print Defect Mapping with Semantic Segmentation

Efficient automated print defect mapping is valuable to the printing industry since such defects directly influence customer-perceived printer quality and manually mapping them is cost-ineffective. Conventional methods consist of complicated and hand-crafted feature engineering techniques, usually t...

Full description

Saved in:
Bibliographic Details
Main Authors: Valente, Augusto C., Wada, Cristina, Neves, Deangela, Neves, Deangeli, Perez, Fabio V. M., Megeto, Guilherme A. S., Cascone, Marcos H., Gomes, Otavio, Lin, Qian
Format: Conference Proceeding
Language:English
Subjects:
Image color analysis
Image segmentation
Printers
Printing
Semantics
Task analysis
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Efficient automated print defect mapping is valuable to the printing industry since such defects directly influence customer-perceived printer quality and manually mapping them is cost-ineffective. Conventional methods consist of complicated and hand-crafted feature engineering techniques, usually targeting only one type of defect. In this paper, we propose the first end-to-end framework to map print defects at pixel level, adopting an approach based on semantic segmentation. Our framework uses Convolutional Neural Networks, specifically DeepLab-v3+, and achieves promising results in the identification of defects in printed images. We use synthetic training data by simulating two types of print defects and a print-scan effect with image processing and computer graphic techniques. Compared with conventional methods, our framework is versatile, allowing two inference strategies, one being near real-time and providing coarser results, and the other focusing on offline processing with more fine-grained detection. Our model is evaluated on a dataset of real printed images.
ISSN:2642-9381
DOI:10.1109/WACV45572.2020.9093470