{"title":"A Hierarchical Feature Fusion-based Method for Defect Recognition with a Small Sample","authors":"Yiping Gao, Liang Gao, Xinyu Li","doi":"10.1109/IEEM44572.2019.8978912","DOIUrl":null,"url":null,"abstract":"As one of the breakthroughs in modern manufacturing, deep learning (DL) performs large-scale network architectures and achieves some outstanding performances in vision-based defect recognition. However, most of these large-scale networks require a large sample for training, and a small sample might cause the networks overfitting and collapse. Since the defect often occurs with a low probability, it is costly to collect large-scale samples. To overcome this problem, a hierarchical feature fusion-based method is introduced for defect recognition with a small sample. The proposed method divides a pretrained VGG16 network into different blocks, and learns the hierarchical features from the low- and high- level blocks. The results are better than the other methods. This result manifests the proposed method suits problem, and the defect recognition could be deployed earlier with the proposed method.","PeriodicalId":255418,"journal":{"name":"2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IEEM44572.2019.8978912","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
As one of the breakthroughs in modern manufacturing, deep learning (DL) performs large-scale network architectures and achieves some outstanding performances in vision-based defect recognition. However, most of these large-scale networks require a large sample for training, and a small sample might cause the networks overfitting and collapse. Since the defect often occurs with a low probability, it is costly to collect large-scale samples. To overcome this problem, a hierarchical feature fusion-based method is introduced for defect recognition with a small sample. The proposed method divides a pretrained VGG16 network into different blocks, and learns the hierarchical features from the low- and high- level blocks. The results are better than the other methods. This result manifests the proposed method suits problem, and the defect recognition could be deployed earlier with the proposed method.