Concrete structure apparent defect identification method and device based on inspection robot

By using inspection robots to collect concrete structure images in real time and using the YOLOv5 model to identify defects, the problems of low efficiency and poor safety of manual identification are solved, and efficient and accurate defect identification is achieved, especially improving robustness in complex environments.

CN120673220AActive Publication Date: 2025-09-19BCEG CIVIL ENGINEERING CO LTD
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Patent Information

Application Number
CN202510553142.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-04-29
Publication Date
2025-09-19
Estimated Expiration
2045-04-29

AI Technical Summary

Technical Problem

In the existing technology, manual identification of apparent defects in concrete structures is time-consuming and labor-intensive, easily affected by subjective factors, and poses safety risks in high-altitude or dangerous environments.

Method used

A method for identifying surface defects of concrete structures based on an inspection robot is adopted. A depth camera is used to collect images in real time, and the defects are identified through the YOLOv5 defect recognition model. LiDAR and light sensors are combined to optimize image quality, and the Transformer module and CBAM module are used to improve recognition accuracy.

Benefits of technology

It improves the recognition efficiency and accuracy of surface defects in concrete structures, reduces the safety risks of manual identification, and enhances the robustness in complex environments, especially the recognition ability of complex lighting and texture images.

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Abstract

The invention discloses a concrete structure apparent defect recognition method and device based on an inspection robot, and the method comprises the steps: collecting an apparent image of a target concrete structure in real time through the inspection robot in response to an apparent defect recognition instruction of the target concrete structure; obtaining a defect identification model based on YOLOv5; inputting the processed apparent image into a defect identification model, carrying out global image feature extraction and context image feature extraction on the apparent image through each Transform module in the backbone network, and carrying out merging processing on the global image features and the context image features through an SPPF module; performing key feature enhancement on the merged image features through a channel attention layer and a space attention layer of each CBAM module in the neck network, performing feature fusion on the enhanced merged image features to obtain fused image features corresponding to each CBAM module, and performing defect identification on each fused image feature through the head network to obtain a fused image feature corresponding to each CBAM module; and obtaining a defect identification result output by each prediction head module.
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