Affiliation:
1. MOE Key Laboratory of Deep Earth Science and Engineering and Department of Civil Engineering, College of Architecture and Environment Sichuan University Chengdu China
2. Shandong Key Laboratory of Intelligent Buildings Technology, School of Information and Electrical Engineering Shandong Jianzhu University Jinan China
Abstract
AbstractAutomatic 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.
Funder
National Natural Science Foundation of China
Taishan Scholar Project of Shandong Province
Natural Science Foundation of Shandong Province
Fundamental Research Funds for the Central Universities
Subject
Computational Theory and Mathematics,Computer Graphics and Computer-Aided Design,Computer Science Applications,Civil and Structural Engineering,Building and Construction
Cited by
2 articles.
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