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Many intelligent detection systems are constructed based on computer. Feature extraction is critical for constructing such system to recognize and classify the weld defects. Deep neural networks have powerful ability to learn representative features that are more sensitive to classification. However, a large number of samples are usually required. In this paper, a stacked autoencoder network is used to pretrain a deep neural network with a small dataset. We can learn the hierarchical feature from the network. In addition, two kinds of traditional manual features are extracted from the same set. These features are combined into new fusion feature vectors for classifying different weld defects. Two evaluation methods are used to test the classification performance of these features through several experiments. The results show that deep feature based on stacked autoencoder network performs better than the other features. The classification performance of fusion features is better than single feature.<\/jats:p>","DOI":"10.1155\/2022\/8088202","type":"journal-article","created":{"date-parts":[[2022,2,23]],"date-time":"2022-02-23T18:11:19Z","timestamp":1645639879000},"page":"1-9","source":"Crossref","is-referenced-by-count":10,"title":["Feature Fusion for Weld Defect Classification with Small Dataset"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7691-5018","authenticated-orcid":true,"given":"Wenhui","family":"Hou","sequence":"first","affiliation":[{"name":"Anhui Provincial Engineering Laboratory of Intelligent Agricultural Machinery, School of Engineering, Anhui Agriculture University, No. 130 West Changjiang Road, Hefei 230026, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lulu","family":"Rao","sequence":"additional","affiliation":[{"name":"Anhui Provincial Engineering Laboratory of Intelligent Agricultural Machinery, School of Engineering, Anhui Agriculture University, No. 130 West Changjiang Road, Hefei 230026, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andong","family":"Zhu","sequence":"additional","affiliation":[{"name":"Anhui Provincial Engineering Laboratory of Intelligent Agricultural Machinery, School of Engineering, Anhui Agriculture University, No. 130 West Changjiang Road, Hefei 230026, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2416-1058","authenticated-orcid":true,"given":"Dashan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Anhui Provincial Engineering Laboratory of Intelligent Agricultural Machinery, School of Engineering, Anhui Agriculture University, No. 130 West Changjiang Road, Hefei 230026, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","reference":[{"issue":"12","key":"1","doi-asserted-by":"crossref","first-page":"7606","DOI":"10.1016\/j.eswa.2010.04.082","article-title":"Multiclass defect detection and classification in weld radiographic images using geometric and texture features","volume":"37","author":"I. 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