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Improved basic elements detection algorithm for bridge engineering design drawings based on YOLOv5

Published online by Cambridge University Press:  16 December 2024

Ning An Open the ORCID record for Ning An [Opens in a new window] ,
Linsheng Huang ,
Mengnan Hu ,
Junan Zhu  and
Chuanjian Wang
Ning An
Affiliation:
School of Electronic Information Engineering, Anhui University, Hefei, Anhui, China
Linsheng Huang
Affiliation:
School of Internet, Anhui University, Hefei, Anhui, China
Mengnan Hu
Affiliation:
School of Internet, Anhui University, Hefei, Anhui, China
Junan Zhu*
Affiliation:
School of Internet, Anhui University, Hefei, Anhui, China
Chuanjian Wang*
Affiliation:
School of Internet, Anhui University, Hefei, Anhui, China
*
Corresponding authors: Junan Zhu and Chuanjian Wang; Emails: [email protected]; [email protected]
Corresponding authors: Junan Zhu and Chuanjian Wang; Emails: [email protected]; [email protected]
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Abstract

Bridge engineering design drawings basic elements contain a large amount of important information such as structural dimensions and material indexes. Basic element detection is seen as the basis for digitizing drawings. Aiming at the problem of low detection accuracy of existing drawing basic elements, an improved basic elements detection algorithm for bridge engineering design drawings based on YOLOv5 is proposed. Firstly, coordinate attention is introduced into the feature extraction network to enhance the feature extraction capability of the algorithm and alleviate the problem of difficult recognition of texture features inside grayscale images. Then, targeting objectives across different scales, the standard 3 × 3 convolution in the feature pyramid network is replaced with switchable atrous convolution, and the atrous rate is adaptively selected for convolution computation to expand the sensory field. Finally, experiments are conducted on the bridge engineering design drawings basic elements detection dataset, and the experimental results show that when the Intersection over Union is 0.5, the proposed algorithm achieves a mean average precision of 93.6%, which is 3.4% higher compared to the original YOLOv5 algorithm, and it can satisfy the accuracy requirement of bridge engineering design drawings basic elements detection.

Keywords

bridge engineering design drawingsbasic elements detectionimproved YOLOv5attention mechanismatrous convolution
Type
Research Article
Information
AI EDAM , Volume 38 , 2024 , e22
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Copyright
© The Author(s), 2024. Published by Cambridge University Press

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