Wiper Arm Defect Detection Using Laplacian Pyramids and Genetic Algorithm

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.

I. Introduction

In an effort to reduce reliance on labour and improve product quality, many processes in the manufacturing industry have been automated using machine vision. A lot of significant progress and success have been achieved in employing machine vision to control the quality of a wide range of products, ranging from automotive parts, tiles, pipes, fabric, food, medical supplies and others. In the mass production of car wiper arms, the traditional human inspection of defects is being practised. It requires good eyesight, a lot of focus, skill and fast judgement on the inspectors. This repetitive work imposes a lot of stress and is mentally exhaustive, which could lead to inconsistent inspection results.