This article's motivation is to robotize after creating quality control of media, basically focal handling device frameworks. It is imperative to make a model that upholds quality control and improves efficiency and speed by eliminating unpredictable items. The most generally utilized innovation for this is the utilization of modern picture preparing, which is introduced in the creation line and depends on the utilization of particular cameras or imaging frameworks. This article proposes an exceptionally effective model dependent on a continuous picture processor and AI. An industry production line such as a media line scans an image as a sample system administrator; any waves as their component cloud systems are displayed by this information degree. Will be transferred to. The correct classification of a machine learning-based approach is used. This model focused on the anomaly and helped set the angle at which the production image taken, and our method did not show 92% accuracy.

本文的动机是在创建媒体的质量控制（基本上是焦点处理设备框架）之后进行自动化。必须建立一个模型来保持质量控制，并通过消除不可预测的项目来提高效率和速度。为此，最广泛使用的创新是现代图片准备的利用，这是在创作过程中引入的，并且取决于特定相机或成像框架的利用。本文提出了一种依赖连续图像处理器和AI的异常有效的模型。诸如媒体生产线之类的工业生产线以样本系统管理员的身份扫描图像；以此信息程度显示作为其组成云系统的任何波浪。将被转移到。使用了基于机器学习的方法的正确分类。该模型着眼于异常情况，并帮助设置了拍摄生产图像的角度，而我们的方法未显示出92％的准确性。