Intelligent Recognition of Geometric Tolerance Symbol Annotation Blocks

In previous mechanical design drawings, the geometric tolerance symbol annotation block was directly annotated on the mechanical design drawing by engineers, resulting in the symbol annotation block being non-editable and non-shareable. To achieve digitization, this study proposes an improved Swin_YOLOv5_GE model based on YOLOv5 to realize the positioning of the symbol annotation block. Compared with the original YOLOv5, the accuracy is increased by 4%. Then, image processing is performed on the located and cropped symbol annotation block to achieve denoising and tilt correction of the symbol annotation block. Finally, it is input into CRNN for the recognition of the symbol annotation block. The final experimental results prove that the method in this paper can obtain an overall recognition accuracy of 94%, proving the feasibility of this method.

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Publication Details

Published
2024-12-20
DOI
https://doi.org/10.1109/aiim64537.2024.10934646
Primary Topic
Manufacturing Process and Optimization
Type
article
Field-Weighted Citation Impact
0.00

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article

Intelligent Recognition of Geometric Tolerance Symbol Annotation Blocks

Yuanping Xu, Chaolong Zhang, Chao Kong, Benjun Guo et al.
Manufacturing Process and Optimization
article

Intelligent Recognition of Geometric Tolerance Symbol Annotation Blocks

Yuanping Xu, Chaolong Zhang, Chao Kong, Benjun Guo, Weiye Wang, Jin Jin, Dan Tang, Zhijie Xu, LI Cong-xin
article en

Abstract

In previous mechanical design drawings, the geometric tolerance symbol annotation block was directly annotated on the mechanical design drawing by engineers, resulting in the symbol annotation block being non-editable and non-shareable. To achieve digitization, this study proposes an improved Swin_YOLOv5_GE model based on YOLOv5 to realize the positioning of the symbol annotation block. Compared with the original YOLOv5, the accuracy is increased by 4%. Then, image processing is performed on the located and cropped symbol annotation block to achieve denoising and tilt correction of the symbol annotation block. Finally, it is input into CRNN for the recognition of the symbol annotation block. The final experimental results prove that the method in this paper can obtain an overall recognition accuracy of 94%, proving the feasibility of this method.

Chengdu University of Information Technology (CN), Xi’an Jiaotong-Liverpool University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 58%
Manufacturing Process and Optimization
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