Automatic Detection Method for Concrete Spalling and Exposed Steel Bars in Reinforced Concrete Structures Based on Machine Vision

被引:0
|
作者
Wang, Shengmin [1 ]
Wan, Jun [1 ]
Zhang, Shiying [2 ]
Du, Yu [3 ,4 ]
机构
[1] Wuhan Text Univ, Sch Accountancy, Wuhan 430200, Peoples R China
[2] Nanchang Univ, Sch Infrastruct Engn, Nanchang 330031, Peoples R China
[3] Wuhan Univ Technol, Sch Civil Engn & Architecture, Wuhan 430062, Peoples R China
[4] Third Construct Engn Co Ltd, China Construct Engn Bur 2, Beijing 100071, Peoples R China
关键词
deep learning; machine vision; exposed steel bars; concrete spalling; CLASSIFICATION;
D O I
10.3390/buildings14061580
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Reinforced concrete (RC), renowned for its amalgamation of strength and durability, stands as a cornerstone in modern engineering, extensively employed in various structures such as buildings, bridges, and pipe culverts. However, prevalent issues of concrete spalling and exposed steel bars within RC structures pose significant challenges. An automated identification methodology is proposed to detect concrete spalling and exposed steel bars, leveraging machine vision technology and deep learning algorithms. Initially, a classifier is utilized to discern concrete spalling areas within the image domain at the image level. Subsequently, a semantic segmentation algorithm is applied to precisely delineate the contours of both concrete spalling areas and exposed steel bars at the pixel level. The efficacy and feasibility of the proposed method are validated through training and testing on both a publicly available dataset and actual RC structure images. The results illustrate that the average detection precision, Intersection over Union (IOU), recall, and F1-score for concrete spalling areas are 0.924, 0.872, 0.937, and 0.925, respectively, while for exposed steel areas, the corresponding values are 0.905, 0.820, 0.899, and 0.855. This method demonstrates promising prospects for wide-ranging applications in defect detection within RC structures.
引用
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页数:15
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