文献类型：期刊文献

英文题名：A Novel Quadrilateral Contour Disentangled Algorithm for Industrial Instrument Reading Detection

作者：Li, Xiang Zeng, Changchang Yao, Yong Qian, Jide Zhang, Haiding Zhang, Sen Yang, Suixian

第一作者：Li, Xiang

通信作者：Li, X[1];Zeng, CC[2]

机构：[1]Sichuan Univ, Sch Mech Engn, Chengdu 610065, Peoples R China;[2]Civil Aviat Flight Univ China, Sch Comp Sci, Guanghan 618307, Peoples R China;[3]Natl Inst Measurement & Testing Technol, Chengdu 610056, Peoples R China;[4]Guizhou Inst Technol, Sch Big Data, Guiyang 550003, Peoples R China

第一机构：Sichuan Univ, Sch Mech Engn, Chengdu 610065, Peoples R China

通信机构：corresponding author), Sichuan Univ, Sch Mech Engn, Chengdu 610065, Peoples R China;corresponding author), Civil Aviat Flight Univ China, Sch Comp Sci, Guanghan 618307, Peoples R China.

年份：2025

卷号：27

期号：2

外文期刊名：ENTROPY

收录：;Scopus(收录号：2-s2.0-85218875760);WOS:【SCI-EXPANDED(收录号:WOS:001431741300001)】；

基金：This research was funded by the Fundamental Research Funds for the Central Universities: No. PHD2023-028; Civil Aviation Flight Technology and Flight Safety Key Laboratory Project (No. FZ2022KF03, No. FZ2022ZX59); Research Project on Talent Policy in Developed Countries, No. 24H03006; Henan Science and Technology Research Project, No. 2025-1058; and in part by Opening project of Henan Provincial Key Laboratory of general aviation technology, No. ZHKF-240205.

语种：英文

外文关键词：instrument reading detection; quadrilateral contour disentangled; MsFPN; PCDR; quadrilateral detector

摘要：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.