Car Wiper Arm Defect Detection Using Gabor Filter In car industry, both accuracy and efficiency are equally crucial at the production line to supply high demands of wiper blades in car industry. Since the current production line is mostly employed using manpower, defect detection of car wiper arm remains a huge impact in the quality control process. However, the consistency and the accuracy of identifying defects are still not up to optimum state as the current defect detection method still heavily relies on human vision. To date, the defect detection of car wipers using machine vision has not been extensively implemented in the current car wipers industry. Hence in this paper, we proposed a very first machine vision method to identify defects on car wiper arm. We implemented Gabor filter to detect different kind of defects on wiper arm, such as scratch, bump, and orange skin. The proposed automated defect detection system for car wiper arm has achieved impressive detection accuracy with 81% within a short average processing time of 3.47 seconds.

A smart surface inspection system using faster RCNN in cloudedge computing environment Abstract   Automated surface inspection has become a hot topic with the rapid development of machine vision technologies. Traditional machine vision methods need experts to carefully craft image features for defect detection. This limits their applications to wider areas. The emerging convolutional neural networks (CNN) can automatically extract features and yield good results in many cases. However, the CNNbased image classification methods are more suitable for flat surface texture inspection. It is difficult to accurately locate small defects in geometrically complex products. Furthermore, the computational power required in CNN algorithms is usually high and it is not efficient to be implemented on embedded hardware. To solve these problems, a smart surface inspection system is proposed using faster RCNN algorithm in the cloudedge computing environment. The faster RCNN as a CNNbased object detection method can efficiently identify defects in complex product images and the cloudedge computing framework can provide fast computation speed and evolving algorithm models. A real industrial case study is presented to illustrate the effectiveness of the proposed method. The results show that the proposed method can provide high detection accuracy within a short time.