Disturbance Reduction in Automated Inspection Systems for Enhanced Robust Image Data Collection

Abstract

This thesis presents a comprehensive study aimed at reducing disturbances in automatedinspection systems to enhance the robustness and accuracy of image data collection. The research focuses on identifying common sources of interference and errors in automated inspection systems, particularly in industrial and manufacturing settings. Through a series of experiments and simulations, the study evaluates various techniques for mitigating these disturbances, including advanced algorithms for image processing, noise reduction, and calibration methods. The core of the thesis lies in developing a novel framework that integrates enhanced sensor technologies, improved image processing algorithms, and machine learning techniques to increase the reliability and efficiency of automated inspection systems. The proposed solution is designed to be adaptable to different types of environments and machinery, ensuring broad applicability. Extensive testing demonstrates that the implemented strategies significantly minimize errors caused by external factors such as lighting variations, vibrations, and other environmental influences. The results indicate a notable improvement in the precision and consistency of image data captured by automated inspection systems. This research not only contributes to the field of automated inspection but also has implications for broader applications where accurate image data collection is critical. The findings offer valuable insights for the development of more robust and efficient automated systems in various industrial applications.