Year: 2025
Type: article
Abstract: With the rapid development of urban construction, the demand for safety monitoring of reinforced concrete structures has been increasing. However, current crack detection methods still struggle with limited accuracy, poor real-time performance, and difficulty recognizing extremely small or low-contrast cracks in complex environments. To address these challenges, this study proposes a new method that combines the improved Visual Geometry Group Network 16, U-Net, and You Only Look Once target detection technologies. A new model for detecting concrete corrosion cracks has been developed based on this method. After 100 training epochs, the model achieved a precision of 94.4% and a loss rate of 2.6%, with an average Intersection over Union exceeding 85.0%. In high-roughness field tests, the proposed model achieved a crack width detection error of ±4.0 mm. For cracks that were soil-covered or partially occluded, the detection errors were ±5.4 mm and ±5.1 mm, respectively. Based on the original model, two additional lightweight variants were constructed, with the inference speeds of the three models recorded as 36 ms, 28 ms, and 24 ms in descending order. The results demonstrate that the proposed detection model offers an efficient and intelligent solution for structural health monitoring, with strong potential for engineering applications and urban infrastructure renewal. However, the model still presents a risk of misclassification when identifying fine cracks under low-contrast or complex background conditions. Future work will incorporate adaptive image enhancement and more refined feature extraction algorithms to further improve detection robustness and real-time performance.
Source: Coatings
Authors: Lingling Chen, Zhiyuan Wang, Huihui Liu
Institutions: Bengbu University, Anhui University of Finance and Economics
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
Funder: Bengbu University
OpenAlex ID: W4410808113