Research on Deep Learning-Based Product Packaging Inkjet Detection Algorithm

During the production process, inkjet characters on product packaging often exhibit defects, such as missing or smudged characters, due to mechanical vibrations and ink quality issues. Therefore, it is necessary to conduct inkjet detection before products leave the factory. Traditional manual inspection methods are inefficient, costly, and prone to fatigue, making them unsuitable for long-term tasks. Moreover, conventional machine vision technologies cannot meet the required detection accuracy for inkjet characters in complex backgrounds. To address the imbalance between speed and accuracy in text detection algorithms under complex conditions, this paper presents a detailed study on inkjet character detection and proposes a lightweight DBNet-based segmentation algorithm that significantly improves detection speed and accuracy.