An Attention-Enhanced Network for Visual Attitude Estimation

Liu, Lu; Duan, Jiahao; Shen, Yaoyang; Wang, Shihan; Mao, Jiale; Liu, Wei; Guo, Yuyan; Wu, Lan; Kong, Ming; Yu, Hang

doi:10.3390/a19040309

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Open AccessArticle

An Attention-Enhanced Network for Visual Attitude Estimation

by

Lu Liu

,

Jiahao Duan

,

Yaoyang Shen

,

Shihan Wang

,

Jiale Mao

,

Wei Liu

,

Yuyan Guo

,

Lan Wu

,

Ming Kong

and

Hang Yu

^*

College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China

^*

Author to whom correspondence should be addressed.

Algorithms 2026, 19(4), 309; https://doi.org/10.3390/a19040309

Submission received: 28 February 2026 / Revised: 9 April 2026 / Accepted: 14 April 2026 / Published: 15 April 2026

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Abstract

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° 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° 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.

Keywords: 3D attitude estimation; attention mechanism; image augmentation; neural network; synthetic dataset

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MDPI and ACS Style

Liu, L.; Duan, J.; Shen, Y.; Wang, S.; Mao, J.; Liu, W.; Guo, Y.; Wu, L.; Kong, M.; Yu, H. An Attention-Enhanced Network for Visual Attitude Estimation. Algorithms 2026, 19, 309. https://doi.org/10.3390/a19040309

AMA Style

Liu L, Duan J, Shen Y, Wang S, Mao J, Liu W, Guo Y, Wu L, Kong M, Yu H. An Attention-Enhanced Network for Visual Attitude Estimation. Algorithms. 2026; 19(4):309. https://doi.org/10.3390/a19040309

Chicago/Turabian Style

Liu, Lu, Jiahao Duan, Yaoyang Shen, Shihan Wang, Jiale Mao, Wei Liu, Yuyan Guo, Lan Wu, Ming Kong, and Hang Yu. 2026. "An Attention-Enhanced Network for Visual Attitude Estimation" Algorithms 19, no. 4: 309. https://doi.org/10.3390/a19040309

APA Style

Liu, L., Duan, J., Shen, Y., Wang, S., Mao, J., Liu, W., Guo, Y., Wu, L., Kong, M., & Yu, H. (2026). An Attention-Enhanced Network for Visual Attitude Estimation. Algorithms, 19(4), 309. https://doi.org/10.3390/a19040309

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An Attention-Enhanced Network for Visual Attitude Estimation

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