# Print Defect Mapping with Semantic Segmentation

arXiv: https://arxiv.org/abs/2001.10111 · HTML: https://mlsanity.com/paper/2001.10111/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jan 2020.

## Built on by (most cited first, 3 of 3)

- [Investigating Shift Equivalence of Convolutional Neural Networks in Industrial Defect Segmentation](https://mlsanity.com/paper/2309.16902/) (2023)
- [A Comprehensive Survey on Machine Learning Driven Material Defect Detection](https://mlsanity.com/paper/2406.07880/) (2024)
- [Synthetic data generation framework for quality control automation in gravure printing](https://mlsanity.com/paper/2607.21577/) (2026)

## Abstract (arXiv)

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.