Wiper Arm Defect Detection Using Laplacian Pyramids and Genetic Algorithm

Abstract

Due to its uneven and curvy surface, researchers had difficulty in getting the wiper arm surface to be evenly illuminated for appearance defect detection using machine vision. As a result, some defects, especially those located at the edge of the region of interest (ROI) were missed. In this paper, the ROI was widened by stitching two sequential images together using Laplacian pyramids. Genetic algorithm was then used to enhance the important features of the defects using the best fitness value, parent mating, crossover and mutation. The algorithm was able to reduce the effect of uneven-illumination by repeating regeneration. The resultant image was converted into binary for defect identification, and localized according to its contour. Experimental results showed 90.5% accuracy.