{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T16:16:47Z","timestamp":1781108207125,"version":"3.54.1"},"reference-count":39,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,10]],"date-time":"2023-05-10T00:00:00Z","timestamp":1683676800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"International Exchanges 2019 Cost Share (NSFC)","award":["IEC\\NSFC\\191320"],"award-info":[{"award-number":["IEC\\NSFC\\191320"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In response to the difficulty of traditional image processing methods to quickly and accurately extract regions of interest from non-contact dorsal hand vein images in complex backgrounds, this study proposes a model based on an improved U-Net for dorsal hand keypoint detection. The residual module was added to the downsampling path of the U-Net network to solve the model degradation problem and improve the feature information extraction ability of the network; the Jensen\u2013Shannon (JS) divergence loss function was used to supervise the final feature map distribution so that the output feature map tended to Gaussian distribution and improved the feature map multi-peak problem; and Soft-argmax is used to calculate the keypoint coordinates of the final feature map to realize end-to-end training. The experimental results showed that the accuracy of the improved U-Net network model reached 98.6%, which was 1% better than the original U-Net network model; the improved U-Net network model file was only 1.16 M, which achieved a higher accuracy than the original U-Net network model with significantly reduced model parameters. Therefore, the improved U-Net model in this study can realize dorsal hand keypoint detection (for region of interest extraction) for non-contact dorsal hand vein images and is suitable for practical deployment in low-resource platforms such as edge-embedded systems.<\/jats:p>","DOI":"10.3390\/s23104625","type":"journal-article","created":{"date-parts":[[2023,5,11]],"date-time":"2023-05-11T01:37:24Z","timestamp":1683769044000},"page":"4625","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Fast and Accurate ROI Extraction for Non-Contact Dorsal Hand Vein Detection in Complex Backgrounds Based on Improved U-Net"],"prefix":"10.3390","volume":"23","author":[{"given":"Rongwen","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Electronic Engineering (College of Artifificial Intelligence), South China Agricultural University, Guangzhou 510642, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangqun","family":"Zou","sequence":"additional","affiliation":[{"name":"Guangzhou Intelligence Oriented Technology Co., Ltd., No. 604, Tian\u2019an Technology Development Building, Tian\u2019an Hi-Tech Ecological Park, No. 555, North Panyu Avenue, Panyu District, Guangzhou 511493, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5588-3443","authenticated-orcid":false,"given":"Xiaoling","family":"Deng","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering (College of Artifificial Intelligence), South China Agricultural University, Guangzhou 510642, China"},{"name":"Lingnan Modern Agriculture Guangdong Laboratory, Guangzhou 510642, China"},{"name":"National International Joint Research Center of Precision Agriculture Aviation Application Technology, Guangzhou 510642, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziyang","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering (College of Artifificial Intelligence), South China Agricultural University, Guangzhou 510642, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifan","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering (College of Artifificial Intelligence), South China Agricultural University, Guangzhou 510642, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengrui","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering (College of Artifificial Intelligence), South China Agricultural University, Guangzhou 510642, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongxin","family":"Xing","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering (College of Artifificial Intelligence), South China Agricultural University, Guangzhou 510642, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fen","family":"Dai","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering (College of Artifificial Intelligence), South China Agricultural University, Guangzhou 510642, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1049\/iet-bmt.2018.5056","article-title":"Towards application of dorsal hand vein recognition under uncontrolled environment based on biometric graph matching","volume":"8","author":"Zhong","year":"2019","journal-title":"IET Biom."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1007\/978-3-030-86608-2_25","article-title":"Dorsal Hand Vein Recognition Based on Transfer Learning with Fusion of LBP Feature","volume":"Volume 12878","author":"Feng","year":"2021","journal-title":"Biometric Recognition"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Li, K., Liu, Q., and Zhang, G. 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