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Traditional neural network techniques have not yet been employed for wood defect detection due to long training time, low recognition accuracy, and nonautomatical extraction of defect image features. In this work, a model (so\u2010called ReSENet\u201018) for wood knot defect detection that combined deep learning and transfer learning is proposed. The \u201csqueeze\u2010and\u2010excitation\u201d (SE) module is firstly embedded into the \u201cresidual basic block\u201d structure for a \u201cSE\u2010Basic\u2010Block\u201d module construction. This model has the advantages of the features that are extracted in the channel dimension, and it is fused in multiscale with original features. Instantaneously, the fully connected layer is replaced with a global average pooling; consequently, the model parameters could be reduced effectively. The experimental results show that the accuracy has reached 99.02%, meanwhile the training time is also reduced. It shows that the proposed deep convolutional neural network based on ReSENet\u201018 combined with transfer learning can improve the accuracy of defect recognition and has a potential application in the detection of wood knot defects.<\/jats:p>","DOI":"10.1155\/2021\/4428964","type":"journal-article","created":{"date-parts":[[2021,8,14]],"date-time":"2021-08-14T18:50:23Z","timestamp":1628967023000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["A Novel Deep Convolutional Neural Network Based on ResNet\u201018 and Transfer Learning for Detection of Wood Knot Defects"],"prefix":"10.1155","volume":"2021","author":[{"given":"Mingyu","family":"Gao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6029-945X","authenticated-orcid":false,"given":"Peng","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junyan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andreas","family":"Mandelis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"DaWei","family":"Qi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,8,14]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"crossref","unstructured":"NorlanderR. 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