{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T18:03:42Z","timestamp":1778004222289,"version":"3.51.4"},"reference-count":54,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,17]],"date-time":"2023-05-17T00:00:00Z","timestamp":1684281600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Heilongjiang Provincial Natural Science Foundation of China","award":["YQ2020C018"],"award-info":[{"award-number":["YQ2020C018"]}]},{"name":"Heilongjiang Provincial Natural Science Foundation of China","award":["2572019BF08"],"award-info":[{"award-number":["2572019BF08"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["YQ2020C018"],"award-info":[{"award-number":["YQ2020C018"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["2572019BF08"],"award-info":[{"award-number":["2572019BF08"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Wood is one of the main building materials. However, defects on veneers result in substantial waste of wood resources. Traditional veneer defect detection relies on manual experience or photoelectric-based methods, which are either subjective and inefficient or need substantial investment. Computer vision-based object detection methods have been used in many realistic areas. This paper proposes a new deep learning defect detection pipeline. First, an image collection device is constructed and a total of more than 16,380 defect images are collected coupled with a mixed data augmentation method. Then, a detection pipeline is designed based on DEtection TRansformer (DETR). The original DETR needs position encoding functions to be designed and is ineffective for small object detection. To solve these problems, a position encoding net is designed with multiscale feature maps. The loss function is also redefined for much more stable training. The results from the defect dataset show that using a light feature mapping network, the proposed method is much faster with similar accuracy. Using a complex feature mapping network, the proposed method is much more accurate with similar speed.<\/jats:p>","DOI":"10.3390\/s23104837","type":"journal-article","created":{"date-parts":[[2023,5,18]],"date-time":"2023-05-18T07:35:50Z","timestamp":1684395350000},"page":"4837","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Wood Veneer Defect Detection Based on Multiscale DETR with Position Encoder Net"],"prefix":"10.3390","volume":"23","author":[{"given":"Yilin","family":"Ge","sequence":"first","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry University, No. 26 Hexing Road, Harbin 150040, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dapeng","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry University, No. 26 Hexing Road, Harbin 150040, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liping","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry University, No. 26 Hexing Road, Harbin 150040, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/S0168-1699(03)00049-8","article-title":"Image segmentation algorithms applied to wood defect detection","volume":"41","author":"Funck","year":"2003","journal-title":"Comput. 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