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This paper focuses on teacher behavior-related object detection in classroom environments and discusses its potential role as a perception module for future metaverse-oriented educational systems. To address challenges such as scale variation, complex backgrounds, and inaccurate localization, an enhanced YOLOv8-based method is proposed. Specifically, an edge-enhanced feature extraction module is introduced to improve boundary representation, and a dynamic inception module is designed to capture multi-scale features. In addition, a Focaler-Shape-IoU loss is adopted to enhance bounding box regression accuracy. These improvements collectively strengthen detection performance. Experiments on a self-collected classroom dataset show that the proposed method achieves competitive results in terms of precision, recall, and mAP@50 compared with several mainstream detectors. The method can effectively handle complex classroom scenarios. Although this work focuses on object detection, it can serve as a foundation for further teacher behavior analysis and may provide foundational perception support for future metaverse-oriented educational applications.<\/jats:p>","DOI":"10.1038\/s41598-026-59541-4","type":"journal-article","created":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T07:38:01Z","timestamp":1782373081000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Teacher behavior-related object detection with an edge-enhanced and dynamic multi-scale network toward metaverse-oriented educational scenarios"],"prefix":"10.1038","volume":"16","author":[{"given":"Jifang","family":"Sun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengbo","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fang","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,25]]},"reference":[{"key":"59541_CR1","doi-asserted-by":"publisher","first-page":"261","DOI":"10.22381\/RCP22202315","volume":"22","author":"M Poliak","year":"2023","unstructured":"Poliak, M., Poliakova, A. & Nica, E. 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