Intelligent Detection of Rebar Size and Position Using Improved DeeplabV3+
-
Published:2023-10-09
Issue:19
Volume:13
Page:11094
-
ISSN:2076-3417
-
Container-title:Applied Sciences
-
language:en
-
Short-container-title:Applied Sciences
Author:
Chen Wei1, Fu Xianglin1, Chen Wanqing2, Peng Zijun1
Affiliation:
1. College of Civil Engineering, Changsha University of Science and Technology, Changsha 410114, China 2. Wuhan Yucheng Jiufang Construction Co., Wuhan 430050, China
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.
Funder
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
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference36 articles.
1. Recent advances in conversational speech recognition using convolutional and recurrent neural networks;Saon;IBM J. Res. Dev.,2017 2. Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (July, January 26). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA. 3. Redmon, J., and Farhadi, A. (2017, January 21–26). YOLO9000: Better, faster, stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA. 4. Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv. 5. Bochkovskiy, A., Wang, C., and Liao, H.M. (2018). Yolov4: Optimal speed and accuracy of object detection. arXiv.
|
|