Abstract
Machine vision systems are applied in industry to control the quality of production while optimizing efficiency. A machine vision and AI-based inspection of color intensity in transparent Polyethylene Terephthalate (PET) preforms is especially sensitive to backgrounds and lighting, therefore, much attention is given to its illumination conditions. The paper examines the adverse factors affecting the quality of image recognition and presents an adaptive method for reducing the influence of changing illumination conditions in the color inspection process of transparent PET preforms. The method is based on predicting measured color intensity correction parameters according to illumination conditions. To test this adaptive method, a hardware and software system for image capture and processing was developed. This system is capable of inspecting large quantities of preforms in real time using a neural network with a modified gradient descent and momentum algorithm. The experiment showed that correction of the measured color intensity value reduced the standard deviation caused by variable and uneven illumination by 61.51%, demonstrating that machine vision color intensity evaluation is a robust and adaptive solution under illuminated conditions for detecting abnormalities in machine-based PET inspection procedures.
Highlights
The world’s tech industry is rapidly changing to Industry 4.0 and fostering a ‘‘smart production’’ initiative aimed at production quality and cost improvements
The software was used during the Polyethylene Terephthalate (PET) preform production process to capture and save color inspection images with a constant color intensity
Variable and uneven illumination of a scene in machine vision has an adverse effect on inspecting color intensity defects by reducing the visual perception capabilities of the computer system
Summary
The world’s tech industry is rapidly changing to Industry 4.0 and fostering a ‘‘smart production’’ initiative aimed at production quality and cost improvements. Major industrial companies are adopting new AI systems to gain an Industry 4.0 advantage. The authors propose a machine vision technique for improving Polyethylene Terephthalate (PET) preform production. Machine vision is a combination of software and hardware that provides operational control in devices for executing functions such as image capture and processing and measurement of critical attributes. In these systems, resolution and sensitivity are the two most important parameters. Resolution is responsible for differentiating between objects, whereas sensitivity is the system’s ability to detect an object despite dim light
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