Resum

Within the framework of quality perception in the market as a differentiating factor for customers, the CICLOP 2.0 project is integrated. The evolution of artificial vision systems, greatly enhanced by the image processing capabilities of deep learning algorithms, enables the development of quality control applications in which the system itself can learn and make decisions based on captured images. In an industry with limited adoption of advanced data processing technologies, the CICLOP 2.0 project aims to develop a quality control system by combining Artificial Vision with Deep Learning and facilitate its implementation in the digital printing sector.

While the initial phase of the CICLOP project aimed to capture large-format images using artificial vision for the detection of printing defects, the second phase seeks to investigate the categorization of defects through the integration of deep learning technology. This need for defect analysis to align quality criteria with customer perception forms the framework of the CICLOP 2.0 project. With the clear goal of reducing the number of incidents in the market, the project will explore how deep learning algorithms can enhance inspection reliability throughout its duration.