{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:23:17Z","timestamp":1792790597926,"version":"4.5.0"},"reference-count":238,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,3]],"date-time":"2026-01-03T00:00:00Z","timestamp":1767398400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European project AIRISE","award":["Grant 579 Agreement ID 101092312"],"award-info":[{"award-number":["Grant 579 Agreement ID 101092312"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The impressive advancements in semi-supervised learning have driven researchers to explore its potential in object detection tasks within the field of computer vision. Semi-Supervised Object Detection (SSOD) leverages a combination of a small labeled dataset and a larger, unlabeled dataset. This approach effectively reduces the dependence on large labeled datasets, which are often expensive and time-consuming to obtain. Initially, SSOD models encountered challenges in effectively leveraging unlabeled data and managing noise in generated pseudo-labels for unlabeled data. However, numerous recent advancements have addressed these issues, resulting in substantial improvements in SSOD performance. This paper presents a comprehensive review of 28 cutting-edge developments in SSOD methodologies, from Convolutional Neural Networks (CNNs) to Transformers. We delve into the core components of semi-supervised learning and its integration into object detection frameworks, covering data augmentation techniques, pseudo-labeling strategies, consistency regularization, and adversarial training methods. Furthermore, we conduct a comparative analysis of various SSOD models, evaluating their performance and architectural differences. We aim to ignite further research interest in overcoming existing challenges and exploring new directions in semi-supervised learning for object detection.<\/jats:p>","DOI":"10.3390\/s26010310","type":"journal-article","created":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T10:53:50Z","timestamp":1767610430000},"page":"310","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Semi-Supervised Object Detection: A Survey on Progress from CNN to Transformer"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7052-979X","authenticated-orcid":false,"given":"Tahira","family":"Shehzadi","sequence":"first","affiliation":[{"name":"Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany"},{"name":"Mindgarage Lab, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany"},{"name":"German Research Institute for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-5055-7352","authenticated-orcid":false,"given":"Ifza","family":"Ifza","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany"},{"name":"Mindgarage Lab, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany"},{"name":"German Research Institute for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4029-6574","authenticated-orcid":false,"given":"Marcus","family":"Liwicki","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Electrical and Space Engineering University of Technology, 971 87 Lule\u00e5, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Didier","family":"Stricker","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany"},{"name":"Mindgarage Lab, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany"},{"name":"German Research Institute for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0536-6867","authenticated-orcid":false,"given":"Muhammad Zeshan","family":"Afzal","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany"},{"name":"Mindgarage Lab, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany"},{"name":"German Research Institute for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Deng, L., and Yu, D. 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