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Entropy
Volume 27
Issue 2
10.3390/e27020122
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

A Novel Quadrilateral Contour Disentangled Algorithm for Industrial Instrument Reading Detection

by
Xiang Li
1,*,
Changchang Zeng
2,*,
Yong Yao
3,
Jide Qian
2,
Haiding Zhang
3,
Sen Zhang
4 and
Suixian Yang
1
1
School of Mechanical Engineering, Sichuan University, Chengdu 610065, China
2
School of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, China
3
National Institute of Measurement and Testing Technology, Chengdu 610056, China
4
School of Big Data, Guizhou Institute of Technology, Guiyang 550003, China
*
Authors to whom correspondence should be addressed.
Entropy 2025, 27(2), 122; https://doi.org/10.3390/e27020122
Submission received: 20 November 2024 / Revised: 14 January 2025 / Accepted: 22 January 2025 / Published: 24 January 2025
(This article belongs to the Special Issue Application of Information Theory to Computer Vision and Image Processing II)
Versions Notes

Abstract

Instrument reading detection in industrial scenarios poses significant challenges due to reading contour distortion caused by perspective transformation in the instrument images. However, existing methods fail to accurately read the display automatically due to incorrect labeling of the target box vertices, which arises from the vertex entanglement problem. To address these challenges, a novel Quadrilateral Contour Disentangled Detection Network (QCDNet) is proposed in this paper, which utilizes the quadrilateral disentanglement idea. First, a Multi-scale Feature Pyramid Network (MsFPN) is proposed for effective feature extraction to improve model accuracy. Second, we propose a Polar Coordinate Decoupling Representation (PCDR), which models each side of the instrument contour using polar coordinates. Additionally, a loss function for the polar coordinate parameters is designed to aid the PCDR in more effectively decoupling the instrument reading contour. Finally, the experimental results on the instrument dataset demonstrate that QCDNet outperforms existing quadrilateral detection algorithms, with improvements of 4.07%, 1.8%, and 2.89% in Precision, Recall, and F-measure, respectively. These results confirm the effectiveness of QCDNet for instrument reading detection tasks.
Keywords: instrument reading detection; quadrilateral contour disentangled; MsFPN; PCDR; quadrilateral detector instrument reading detection; quadrilateral contour disentangled; MsFPN; PCDR; quadrilateral detector

Share and Cite

MDPI and ACS Style

Li, X.; Zeng, C.; Yao, Y.; Qian, J.; Zhang, H.; Zhang, S.; Yang, S. A Novel Quadrilateral Contour Disentangled Algorithm for Industrial Instrument Reading Detection. Entropy 2025, 27, 122. https://doi.org/10.3390/e27020122

AMA Style

Li X, Zeng C, Yao Y, Qian J, Zhang H, Zhang S, Yang S. A Novel Quadrilateral Contour Disentangled Algorithm for Industrial Instrument Reading Detection. Entropy. 2025; 27(2):122. https://doi.org/10.3390/e27020122

Chicago/Turabian Style

Li, Xiang, Changchang Zeng, Yong Yao, Jide Qian, Haiding Zhang, Sen Zhang, and Suixian Yang. 2025. "A Novel Quadrilateral Contour Disentangled Algorithm for Industrial Instrument Reading Detection" Entropy 27, no. 2: 122. https://doi.org/10.3390/e27020122

APA Style

Li, X., Zeng, C., Yao, Y., Qian, J., Zhang, H., Zhang, S., & Yang, S. (2025). A Novel Quadrilateral Contour Disentangled Algorithm for Industrial Instrument Reading Detection. Entropy, 27(2), 122. https://doi.org/10.3390/e27020122

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