A machine learning based quality control system for power cable manufacturing We study methods for observing physical defects on the surface of power cables. Quality control is essential for power cable manufacturing and surface defects are an important quality factor. Traditionally power cable manufacturing has relied on manual inspection as automated methods have not been sufficient to be used in industrial production.We have designed and implemented a novel defect detection system that applies machine learning methods to detect power cable surface defects. Our system uses laser scanning to map the surface of a cable during production. For the machine learning, we have evaluated different CNN (Convolutional Neural Network) architectures and studied their performance and accuracy. According to our results, CNNs are suitable for the detection of surface defects as they can be trained with large amounts of cable surface data.

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.