Detect Microscopic Motion in Mechanical Equipment using Deep-Learning

ABSTRACT: Subtle movements occurring in our environment often go unnoticed due to their minute, microscopic nature, rendering them undetectable by the human eye. However, if these imperceptible motions could be magnified, a significant amount of valuable information could be extracted. Traditional motion magnification techniques relied on carefully designed filters to detect specific motions and frequencies of interest. Despite their utility, these methods were limited to identifying only certain frequency ranges and exhibited poor performance when confronted with large motions. The advent of deep learning and advancements in computer vision have revolutionized this process, allowing for the automatic design of filters through machine learning models trained to detect and amplify subtle motions effectively, regardless of the frequency or the scale of the motion.