Research on the Optimization of Defect Detection Based on Convolutional Neural Network Architecture

Abstract

Machine learning is a method generally used in defect detection of smart manufacturing. It uses data and algorithms to simulate the function of human’s brain, and the accuracy can be improved by repeated machine training. Neural networks such as convolutional neural network is an effective method used in machine learning to achieve defect detection in smart manufacturing. Through a systematic lecture review, this gives the architecture of the convolutional network and provides its model, which includes three layers: convolution layer, pooling layer and fully-connected layer, whose functions are determining the way of the neurons network connections, reducing the amount of parameters within activations by downsampling along with the spatial dimensionality of the inputs and produce class scores from the activations as well as suggesting the usage of Rectified Linear Unit to improve performance respectively, then points out two challenges of applying convolutional neural network in practical of defect detection, which is the class imbalance problem and insufficient data problem. Following these problems, three solutions namely, Objectlevel Attention Mechanism; PaDiM and SDD-CNN are discussed. In addition, this paper also identifies the topics for future study at the end.