Intelligent Gear Inspection: Defect Detection through Digital Image Processing Algorithms

Abstract

A significant contributor to poor reliability and a source of humiliation for businesses are gear problems. The majority of the inspection procedures used in these businesses are laborious and manually. More thorough and accurate inspection procedures are needed to improve accuracy in finding gear problems. The present work fills this gap by implementing a Gear Defect Recognizer that uses local thresholding in conjunction with computer vision methodology to spot potential flaws. The recognizer creates a less error-prone examination system in real time while identifying the gear flaws at a reasonable cost. The recognizer mostly employs an image collecting device to record physical gear images and then transforms the RGB photos into binary images using local filtering approaches and restoration processes. Later, the outputs of the processed image are the area of the faulty portion and compute the possible defective and non –defective gear as an output