A method of concrete damage detection and localization based on weakly supervised learning

计算机科学 像素 人工智能 卷积神经网络 跳跃式监视 相似性(几何) 最小边界框 模式识别(心理学) 自动化 计算机视觉 数据挖掘 图像(数学) 工程类 机械工程
作者
Yongqing Jiang,Dongfang Pang,Chengdong Li,Jianze Wang
出处
期刊:Computer-aided Civil and Infrastructure Engineering [Wiley]
卷期号:39 (7): 1042-1060 被引量:1
标识
DOI:10.1111/mice.13124
摘要

Abstract Automatic inspection of concrete surface defects based on visual elements is crucial for the timely detection of security risks in infrastructure. Moreover, accurate determination of the geographical location of the detected defects is critical for subsequent maintenance and reinforcement tasks. This study employed convolutional neural network (CNN) training methods for detection and localization. This approach employs bounding boxes to confine damaged pixels and utilizes projection loss to foster similarity learning between pixels. In addition, geotags are automatically indexed through the vectorization of invariant features in a scene, which maintains high model accuracy and reduces training costs. The proposed method can detect and classify typical concrete defects in complex scenarios and accurately locate them without the use of external sensors. In addition, the proposed model can achieve pixel‐level defect detection and geographic location determination through the cost of bounding box annotation and automatic indexing. The proposed model was evaluated using 10,691 images of four typical concrete defects in various complex environments. The results demonstrate that the proposed method achieves a pixel‐level detection accuracy of 48.75 and a location accuracy of 83.69, showing a better performance than other methods.
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