Intelligent Detection of Rebar Size and Position Using Improved DeeplabV3+
Source abstract
Abstract
For the development of reinforced concrete structures and infrastructure construction, traditional rebar checking and acceptance methods have shortcomings in terms of efficiency. The use of digital image processing technology cannot easily identify a rebar configuration with complex and diverse backgrounds. To solve this problem, an inspection method combining deep learning and digital image processing techniques is proposed using an improved DeeplabV3+ model to identify reinforcing bars, with the identification results subjected to digital image processing operations to obtain the size information of the reinforcing bar. The proposed method was validated through a field test. The results of the experiment indicated that the proposed model is more accurate than other models, with a mean Intersection over Union (mIoU), precision, recall, and F1 score reaching 94.62%, 97.42%, 96.95%, and 97.18%, respectively. Moreover, the accuracy of the dimension estimations for the test reinforcements met the engineering acceptance standards.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Infrastructure Maintenance and Monitoring · Structural Health Monitoring Techniques · Industrial Vision Systems and Defect Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Civil Engineering, Changsha University of Science and Technology, Changsha 410114, China
Wuhan Yucheng Jiufang Construction Co., Wuhan 430050, China
Funding
National Natural Science Foundation of China · Changsha University of Science and Technology Innovative Project of Civil Engineering Excellent Characteristic Key Discipline · Open Fund Project of Changsha University of Science and Technology in the field of Bridge Engineering
References (36)
Show source-provided references
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