AI-Based Vision System for an Uncertain Dynamic Environment

Abstract

Machine vision systems are increasingly expected to operate in practical environments where lighting conditions, object movement, and surrounding scenes may change continuously. Such variations often influence recognition reliability and create additional difficulties in applications including industrial automation, surveillance, and autonomous navigation. The study examines an AI-supported machine vision framework designed for operation under dynamic situations. Adaptive preprocessing, deep learning–based recognition, and environmental compensation mechanisms were incorporated within the proposed approach. Feature extraction relied on convolutional neural networks, while recurrent learning models assisted in tracking moving objects across image sequences. Reinforcement learning was also used to adjust system parameters according to observed scene behavior. Evaluation under changing lighting situations and dynamic operating scenarios showed recognition accuracy above 92%, whereas conventional methods produced noticeably lower performance under similar settings. Observations from the experiments indicate that adaptive AI mechanisms can contribute to improved reliability of machine vision systems operating in real-world environments.