Paper Title
Implementation of Artificial Intelligence to Detect Images in The Manufacturing Process

Automation of the production of ever more complex products brings along an increase in product control requirements. In addition, industry, especially automotive, requires a further increase in production accuracy, minimization of downtime and delivery by just-in-time. Quality control by accidental selection and by mechanical devices is not sufficient to meet the production requirements. In most cases, visual quality control is ensured by people. This article describes the basic paradigms of using artificial intelligence systems for quality control detection. To describe the deployment techniques of artificial intelligence elements, the RetinaNet Convoluntary Neural Network (CNN) was used. Index Terms - Neural Networks, Quality Control, Error Detection.