# Print Defect Mapping with Semantic Segmentation Source: [Print Defect Mapping with Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:04:19.938Z Authors: Augusto C. Valente, Cristina Wada, Deangela Neves, Deangeli Neves, F\\'abio V. M. Perez, Guilherme A. S. Megeto, Marcos H. Cascone, Otavio Gomes, Qian Lin Published: 2020-01-27T22:40:09.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()