{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T02:57:28Z","timestamp":1776913048733,"version":"3.51.2"},"reference-count":26,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T00:00:00Z","timestamp":1722211200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Art Fund","award":["2023-A-05-018-472"],"award-info":[{"award-number":["2023-A-05-018-472"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Traditional image classification usually relies on manual feature extraction; however, with the rapid development of artificial intelligence and intelligent vision technology, deep learning models such as CNNs can automatically extract key features from input images to achieve efficient classification. This study focuses on the application of lightweight separable convolutional neural networks in domain-specific image classification tasks. In this paper, we discuss how to use the SSDLite object detection algorithm combined with the MobileNetV2 lightweight convolutional architecture for puppet dynasty recognition from images\u2014a novel and challenging task. By constructing a system that combines object detection and image classification, we aimed to solve the problem of automatic puppet dynasty recognition to reduce manual intervention and improve recognition efficiency and accuracy. We hope that this will have significant implications in the fields of cultural protection and art history research.<\/jats:p>","DOI":"10.3390\/e26080645","type":"journal-article","created":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T16:37:17Z","timestamp":1722271037000},"page":"645","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Puppet Dynasty Recognition System Based on MobileNetV2"],"prefix":"10.3390","volume":"26","author":[{"given":"Xiaona","family":"Xie","sequence":"first","affiliation":[{"name":"Art College, Northeastern University, No. 11 Lane, Wenhua Road, Heping District, Shenyang 110819, China"}]},{"given":"Zeqian","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Sciences, Northeastern University, No. 11 Lane, Wenhua Road, Heping District, Shenyang 110819, China"}]},{"given":"Yuanshuai","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Artificial Intelligence, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100191, China"}]},{"given":"Haoyue","family":"Fu","sequence":"additional","affiliation":[{"name":"College of Sciences, Northeastern University, No. 11 Lane, Wenhua Road, Heping District, Shenyang 110819, China"}]},{"given":"Mengqi","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Sciences, Northeastern University, No. 11 Lane, Wenhua Road, Heping District, Shenyang 110819, China"}]},{"given":"Yingqin","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Sciences, Northeastern University, No. 11 Lane, Wenhua Road, Heping District, Shenyang 110819, China"}]},{"given":"Jinbo","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering and Automation, Northeastern University, No. 11 Lane, Wenhua Road, Heping District, Shenyang 110819, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. 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