Michał Affek, Manuel Barusco, Dina Dupļevska, Kārlis Freivalds, Gergely Hollósi, Corinna Kofler, Michał Kopczyński, Ruba Almahasneh, Philip Newton, Guntis V. Strazds, Gian Antonio Susto, Gábor Szűcs, and Marek S. Tatara
Image Processing: a Review on Industrial Applications
Image processing has become a key technology in industrial automation, supporting quality control, robotic manipulation, and process monitoring. However, deploying deep learning-based vision systems on the factory floor remains challenging. Industrial datasets are often small, imbalanced, and expensive to annotate, while production environments differ substantially from the everyday imagery used to pretrain most vision models. This paper reviews recent advances in industrial image processing and summarizes practical experience from several use cases developed within the AIMS5.0 project. We examine methods for synthetic data generation, multi-camera tracking, and continual learning, with particular attention to their role in data-scarce and evolving production settings. The presented applications cover robotic manipulation, quality inspection, assembly monitoring, and data annotation. For robotic grasping, a hybrid pose estimation pipeline combines AI-based detection and segmentation with Iterative Closest Point fitting. It achieves accuracy comparable to state-of-the-art approaches while requiring only an object point cloud instead of a textured CAD model and offers substantially faster execution. A segmentationbased method for bottle socket detection also provides promising localization accuracy, although further improvements are needed to meet strict mechanical tolerances. For industrial inspection, we show that class balancing, moderate augmentation, and transfer learning improve semiconductor defect classification under severe class imbalance. We further introduce a CycleGAN-based domain adaptation approach that is effective when source and target domains differ in their label distributions. In aluminium corrosion detection, supervised segmentation clearly outperforms unsupervised anomaly detection. Experiments on luminaire assembly show that video action recognition performs well on familiar products but generalizes poorly to visually different variants, illustrating the limitations of vision-only approaches in high-mix, low-volume production. Finally, an iterative pseudolabeling pipeline substantially improves object detection from a small initial set of annotated images. Based on these results, we identify several open challenges in this area, including data requirements, extreme class imbalance, reliable pseudolabel confidence estimation, automatic image-quality filtering, and scalable human-in-the-loop annotation.
Reference:
DOI: 10.36244/ICJ.2026.5.2
Please cite this paper the following way:
Michał Affek, Manuel Barusco, Dina Dupļevska, Kārlis Freivalds, Gergely Hollósi, Corinna Kofler, Michał Kopczyński, Ruba Almahasneh, Philip Newton, Guntis V. Strazds, Gian Antonio Susto, Gábor Szűcs, and Marek S. Tatara "Image Processing: a Review on Industrial Applications", Infocommunications Journal, Industrial AI for Industry 5.0, 2026, pp. 25-43., https://doi.org/10.36244/ICJ.2026.5.2