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A platform for research: civil engineering, architecture and urbanism
Investigation of steel frame damage based on computer vision and deep learning
Abstract Visual damage inspection of steel frames by eyes alone is time-consuming and cumbersome; therefore, it produces inconsistent results. Existing computer vision-based methods for inspecting civil structures using deep learning algorithms have not reached full maturity in exactly locating the damage. This paper presents a deep convolutional neural network-based damage locating (DCNN-DL) method that classifies the steel frame images provided as inputs as damaged and undamaged. DenseNet, a DCNN architecture, was trained to classify the damage. The DenseNet output was upscaled and superimposed on the original image to locate the damaged part of the steel frame. The DCNN-DL method was validated using 144 training and 114 validation sets of steel frame images. DenseNet, with an accuracy of 99.3%, outperformed MobileNet and ResNet with accuracies of 96.2% and 95.4%, respectively. This case study confirms that the DCNN-DL method effectively facilitates the real-time inspection and location of steel frame damage.
Highlights Deep learning approaches is proposed to locate the damaged part of the steel frame. DenseNet based deep convolutional neural network is implemented and validated. Data augmentation and Grad-CAM visualization techniques is implemented. The proposed model accurately locates the damages in the steel structures.
Investigation of steel frame damage based on computer vision and deep learning
Abstract Visual damage inspection of steel frames by eyes alone is time-consuming and cumbersome; therefore, it produces inconsistent results. Existing computer vision-based methods for inspecting civil structures using deep learning algorithms have not reached full maturity in exactly locating the damage. This paper presents a deep convolutional neural network-based damage locating (DCNN-DL) method that classifies the steel frame images provided as inputs as damaged and undamaged. DenseNet, a DCNN architecture, was trained to classify the damage. The DenseNet output was upscaled and superimposed on the original image to locate the damaged part of the steel frame. The DCNN-DL method was validated using 144 training and 114 validation sets of steel frame images. DenseNet, with an accuracy of 99.3%, outperformed MobileNet and ResNet with accuracies of 96.2% and 95.4%, respectively. This case study confirms that the DCNN-DL method effectively facilitates the real-time inspection and location of steel frame damage.
Highlights Deep learning approaches is proposed to locate the damaged part of the steel frame. DenseNet based deep convolutional neural network is implemented and validated. Data augmentation and Grad-CAM visualization techniques is implemented. The proposed model accurately locates the damages in the steel structures.
Investigation of steel frame damage based on computer vision and deep learning
Kim, Bubryur (author) / Yuvaraj, N. (author) / Park, Hee Won (author) / Preethaa, K.R. Sri (author) / Pandian, R. Arun (author) / Lee, Dong-Eun (author)
2021-09-01
Article (Journal)
Electronic Resource
English
Structural Damage Detection of Steel Corrugated Panels Using Computer Vision and Deep Learning
| Springer Verlag | 2024
| BASE | 2023
| DOAJ | 2022