Surface defects classification of hot rolled strip based on improved convolutional neural network

Abstract

Surface defect classification of hot-rolled strip based on machine vision is a challenge task caused by the diversity of defect morphology, high inter-class similarity, and the real-time requirements in actual production. In this work, VGG16-ADB, an improved VGG16 convolution neural network, is proposed to address the problem of defect identification of hot-rolled strip. The improved network takes VGG16 as the benchmark model, reduces the system consumption and memory occupation by reducing the depth and width of network structure, and adds the batch normalization layer to accelerate the convergence speed of the model. Based on a standard dataset NEU, the proposed method can achieve the classification accuracy of 99.63% and the recognition speed of 333 FPS, which fully meets the requirements of detection accuracy and speed in the actual production line. The experimental results also show the superiority of VGG16-ADB over existing classification models for surface defect classification of hot-rolled strip.