Recognition of surface defects of aluminum profiles based on convolutional neural network

Abstract

Many manufacturers will strictly control the quality of products, especially the surface quality of products. Under the same conditions, the better the surface quality of the product, the more competitive it is. Many aluminum profile benchmarking companies have pain points with flaws on the surface of their products. Due to the work mistakes of the workers in the production workshop, unqualified aluminum materials need to be eliminated in the product production control, and the traditional method is to rely on the assembly line workers to check one by one. As the company’s production automation continues to increase, the shortcomings of manual inspection methods have become increasingly prominent. Aiming at the common types of surface defects in the company’s aluminum profile production process, this paper introduces the deep learning method into the identification of aluminum profile surface defects and uses convolutional neural network to identify the surface defects of aluminum profiles. The advantages and disadvantages of different aluminum profile surface defect recognition models such as AlexNet, VGG19 and Inception V4 are analyzed. Finally, according to the recognition effect of the aluminum profile data set, the recognition model of aluminum profile surface defects based on Inception V4 is selected as the optimal model.