Aluminum Foil Surface Defect Recognition Method Based on CNN In order to accurately identify the defects on the aluminum surface, including perforation, stains, yellow spots and scratches, etc., a method combining the robust principal component analysis and the convolution neural network is proposed to detect defects on the surface of aluminum foil products. Firstly, the defect area image of aluminum foil was segmented by using the method of robust principal component analysis; Then, using TensorFlow platform to build CNN network model, loading aluminum foil images for training, and save the network model parameters of training results, on the basis of this network model, loading aluminum foil images acquired in real time and classify them, complete defect detection tasks. Experimental results showed that the proposed algorithm has the following advantages such as high accuracy, favorable expansibility and so on, it can be easily applied into surface defect detection for other objects.

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.