|Table of Contents|

Research on measurement algorithm for double-layer rebar dimensions of floor slabs based on Res-AA U-Net model(PDF)

《建筑科学与工程学报》[ISSN:1673-2049/CN:61-1442/TU]

Issue:
2025年03期
Page:
103-114
Research Field:
建筑结构
Publishing date:

Info

Title:
Research on measurement algorithm for double-layer rebar dimensions of floor slabs based on Res-AA U-Net model
Author(s):
CHEN Wanqing1;  LI Gang1;  SHENG Minghui1;  FU Xianglin2;  CHEN Wei2
(1.Wuhan Yucheng Jiufang Construction Co. Ltd., Wuhan 430050, Hubei, China; 2. School of Civil and Environmental Engineering, Changsha University of Science & Technology, Changsha 410114, Hunan, China)
Keywords:
floor slab double-layer rebar;  dimension measurement;  U-Net model;  attention gate mechanism;  ResNet34 network;  transfer learning;  ASPP module
PACS:
TU398
DOI:
10.19815/j.jace.2024.01062
Abstract:
Rebar engineering inspection has the problem of high consumption of human resources and high time cost for acceptance. Especially when the size acceptance of floor slab double-layer rebar, the upper layer of reinforcing bars covers the lower layer of reinforcing bars, leading to increased difficulty in measurement. However, the traditional image processing methods are difficult to meet the requirements of the measurement accuracy. Therefore, an automatic method for measuring the size of double-layer reinforcing bars in floor slabs based on the Res-AA U-Net was proposed. The method improved Resnet34 by pruning its network structure and optimizing the loss function, replaced the feature extractor of U-Net with the improved Resnet34, replaced the jump connection with the attention gate mechanism, and at the same time added the improved ASPP module at the bottom of the U-Net to construct a dataset containing 3 355 images of floor rebars, and finally accelerated the training speed of the model by using the transfer learning technique. The results show that the model rebar segmentation based on Res-AA U-Net is better than the classical segmentation networks such as U-Net, Deeplab v3+, HRNet, PSPNet, et al. Key metrics including average intersection over union, pixel accuracy, and recall achieve values of 92.81%, 96.02%, and 94.49%, respectively. Compared with the original U-Net, Res-AA U-Net rebar diameter measurement and rebar spacing measurement errors are reduced by 13.63% and 5.82%, respectively, and the measurement accuracy meets the acceptance requirements of double floor rebar in the acceptance standard of rebar engineering, which can effectively improve the efficiency and intelligence of rebar engineering acceptance.

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Last Update: 2025-06-01