Classification of Steel Strip Surface Defects Based on Optimized ResNet18

Zhuangzhuang Hao, Fuji Ren, Xin Kang, Hongjun Ni, Shuaishuai Lv, Hui Wang
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引用次数: 3

Abstract

Steel strip is one of the main products of traditional steel manufacturing enterprises. It is of great significance to accurately identify the types of defects on the surface of the steel strip. This paper innovatively proposes a pre-training method of network weights based on ResNet18. The network is optimized by dynamically adjusting the learning rate. This method can classify steel strip images with high accuracy of 98.585%, avoid overfitting and enhance the stability of training process.
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基于优化后的ResNet18的钢带表面缺陷分类
带钢是传统钢铁生产企业的主要产品之一。准确识别带钢表面缺陷的类型具有重要意义。本文创新性地提出了一种基于ResNet18的网络权值预训练方法。通过动态调整学习率来优化网络。该方法能以98.585%的准确率对钢带图像进行分类,避免了过拟合,增强了训练过程的稳定性。
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