{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T14:27:46Z","timestamp":1781533666522,"version":"3.54.5"},"reference-count":41,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T00:00:00Z","timestamp":1776211200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["12202428"],"award-info":[{"award-number":["12202428"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"award":["12202428"],"award-info":[{"award-number":["12202428"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"name":"the National Key Research and Development Program of China","award":["2024YFF0617002"],"award-info":[{"award-number":["2024YFF0617002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Accurate estimation of object attitude is essential for understanding motion behavior and achieving dynamic tracking. Existing image-based methods often suffer from low efficiency and limited accuracy, while the potential of deep learning has not been fully exploited in this field. To address these limitations, a lightweight deep learning method for attitude estimation is proposed and validated on spherical particles. A synthetic dataset is generated through VTK-based rendering and automatic annotation, providing large-scale training samples with known Euler angles. An improved MobileNetV1 backbone is developed by integrating Squeeze-and-Excitation blocks, a dual-scale Pyramid Pooling Module, global average pooling, and a regression-oriented multilayer perceptron, which enhances feature extraction and enables direct Euler angle prediction. Experimental results show that the proposed method achieves an average error of 0.308\u00b0 on synthetic test images. Furthermore, a solid particle was fabricated through 3D printing and physical measurements were conducted, where the network combined with image preprocessing and augmentation achieved an average error of about 0.5\u00b0 on real images, demonstrating a lightweight and deployment-friendly framework for practical attitude estimation. The results verify the effectiveness of the method and demonstrate its potential for accurate and computationally efficient attitude measurement in applications such as fluid dynamics, industrial inspection, and motion tracking.<\/jats:p>","DOI":"10.3390\/a19040309","type":"journal-article","created":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T14:02:33Z","timestamp":1776261753000},"page":"309","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Attention-Enhanced Network for Visual Attitude Estimation"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5899-4816","authenticated-orcid":false,"given":"Lu","family":"Liu","sequence":"first","affiliation":[{"name":"College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9494-7704","authenticated-orcid":false,"given":"Jiahao","family":"Duan","sequence":"additional","affiliation":[{"name":"College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaoyang","family":"Shen","sequence":"additional","affiliation":[{"name":"College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shihan","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiale","family":"Mao","sequence":"additional","affiliation":[{"name":"College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuyan","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lan","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4437-2212","authenticated-orcid":false,"given":"Ming","family":"Kong","sequence":"additional","affiliation":[{"name":"College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hang","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"033906","DOI":"10.1063\/1.3554304","article-title":"International Collaboration for Turbulence. 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