A Method for Detecting Surface Corrosion Degree of Steel Structures Based on Computer Vision

LU Peng; ZHAO Tiansong; WANG Jian; CHANG Haosong; ZHENG Yun; LIU Xiaolan

doi:10.3724/j.gyjzG23062009

Volume 54 Issue 8

Aug. 2024

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Article Navigation > INDUSTRIAL CONSTRUCTION > 2024 > 54(8): 133-139

Zhang Tiejun, Yan Yuelan. STUDY ON LOCALIZATION OF MANUFACTURE OF UNDERRELAXATION PRESTRESSED STEEL SHANDS FOR CONTAINMENT OF Ling'ao NUCLEAR POWER PLANT (PHASE-Ⅱ) AND ITS USE[J]. INDUSTRIAL CONSTRUCTION, 2009, 39(4): 61-66. doi: 10.13204/j.gyjz200904015

Citation:

LU Peng, ZHAO Tiansong, WANG Jian, CHANG Haosong, ZHENG Yun, LIU Xiaolan. A Method for Detecting Surface Corrosion Degree of Steel Structures Based on Computer Vision[J]. INDUSTRIAL CONSTRUCTION, 2024, 54(8): 133-139. doi: 10.3724/j.gyjzG23062009

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A Method for Detecting Surface Corrosion Degree of Steel Structures Based on Computer Vision

doi: 10.3724/j.gyjzG23062009

1. Inspection and Certification Co., Ltd., MCC, Beijing 100088, China;
2. Tianjin Chengjian University, Tianjin 300384, China;
3. Central Research Institute of Building and Construction Co., Ltd., MCC Group, Beijing 100088, China

Received Date: 2023-06-20
Available Online: 2024-09-19

Abstract

Abstract

Traditional manual inspection methods for surface corrosion damage in steel structures are time-consuming, labor-intensive, and limited by the technical expertise of the inspection personnel. Computer vision technology provides a fast and accurate alternative method for detecting and classifying the surface corrosion on steel structures. Currently, commonly used methods for corrosion degree detection are based on convolutional neural network (CNN) structures. However, due to inherent flaws in the network structure, there is a problem of neglecting certain rust features in the image during corrosion degree classification, leading to incorrect detection results. A steel structure surface corrosion degree recognition method based on the Vision Transformer network structure was proposed. By introducing self-attention mechanisms (SA) during the feature extraction process, data integrity could be ensured. The proposed method was validated on a self-built dataset of rust severity images, achieving an accuracy ratio of 90% in corrosion degree classification. Furthermore, a steel structure surface corrosion degree detection method based on the sliding-window method was also proposed. This method involves segmenting the steel structure images to be inspected, utilizing a trained network structure for corrosion degree detection, and reassembling the detected images to achieve intelligent detection of corrosion degree on the surface of steel structures.
- steel structure,
- surface corrosion,
- damage detection,
- computer vision,
- sliding-window method

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References(22)

References

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