Affiliation:
1. College of Hydraulic and Environmental Engineering, China Three Gorges University, Yichang 443002, China
2. Hubei Key Laboratory of Construction and Management in Hydropower Engineering, China Three Gorges University, Yichang 443002, China
3. State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100048, China
Abstract
The timely identification and detection of surface cracks in concrete dams, an important public safety infrastructure, is of great significance in predicting engineering hazards and ensuring dam safety. Due to their low efficiency and accuracy, manual detection methods are gradually being replaced by computer vision techniques, and deep learning semantic segmentation methods have higher accuracy and robustness than traditional image methods. However, the lack of data images and insufficient detection performance remain challenges in concrete dam surface crack detection scenarios. Therefore, this paper proposes an intelligent detection method for concrete dam surface cracks based on two-stage transfer learning. First, relevant domain knowledge is transferred to the target domain using two-stage transfer learning, cross-domain and intradomain learning, allowing the model to be fully trained with a small dataset. Second, the segmentation capability is enhanced by using residual network 50 (ResNet50) as a UNet model feature extraction network to enhance crack feature information extraction. Finally, multilayer parallel residual attention (MPR) is integrated into its jump connection path to improve the focus on critical information for clearer fracture edge segmentation. The results show that the proposed method achieves optimal mIoU and mPA of 88.3% and 92.7%, respectively, among many advanced semantic segmentation models. Compared with the benchmark UNet model, the proposed method improves mIoU and mPA by 4.6% and 3.2%, respectively, reduces FLOPs by 36.7%, improves inference speed by 48.9%, verifies its better segmentation performance on dam face crack images with a low fine crack miss detection rate and clear crack edge segmentation, and achieves an accuracy of over 85.7% in crack area prediction. In summary, the proposed method has higher efficiency and accuracy in concrete dam face crack detection, with greater robustness, and can provide a better alternative or complementary approach to dam safety inspections than the benchmark UNet model.
Funder
the National Natural Science Foundation of China
the Natural Science Foundation of Hubei Province
the Major Science and Technology Projects of the Ministry of Water Resources
Subject
Water Science and Technology,Aquatic Science,Geography, Planning and Development,Biochemistry
Reference72 articles.
1. A Pixel-Level morphological segmentation and feature quantification method for hydraulic concrete cracks;Ren;J. Hydropower,2021
2. A review on thermo-mechanical modelling of arch dams during construction and operation: Effect of the reference temperature on the stress field;Salazar;Arch. Comput. Methods Eng.,2020
3. An underwater dam crack image segmentation method based on multi-level adversarial transfer learning;Fan;Neurocomputing,2022
4. Image stitching of underwater dam cracks based on connected domain a priori;Huang;Chin. Body Vis. Image Anal.,2020
5. A review of research on concrete dam cracking morphology and its hazard analysis methods;Xu;J. Water Resour. Water Eng.,2016
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