{"title":"基于优化后的ResNet18的钢带表面缺陷分类","authors":"Zhuangzhuang Hao, Fuji Ren, Xin Kang, Hongjun Ni, Shuaishuai Lv, Hui Wang","doi":"10.1109/ICA54137.2021.00018","DOIUrl":null,"url":null,"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.","PeriodicalId":273320,"journal":{"name":"2021 IEEE International Conference on Agents (ICA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Classification of Steel Strip Surface Defects Based on Optimized ResNet18\",\"authors\":\"Zhuangzhuang Hao, Fuji Ren, Xin Kang, Hongjun Ni, Shuaishuai Lv, Hui Wang\",\"doi\":\"10.1109/ICA54137.2021.00018\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":273320,\"journal\":{\"name\":\"2021 IEEE International Conference on Agents (ICA)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE International Conference on Agents (ICA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICA54137.2021.00018\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE International Conference on Agents (ICA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICA54137.2021.00018","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Classification of Steel Strip Surface Defects Based on Optimized ResNet18
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.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
