Abstract
Crack plays a critical role in the field of evaluating the quality of concrete structures, which affects the safety, applicability, and durability of the structure. Due to its excellent performance in image processing, the convolutional neural network is becoming the mainstream choice to replace manual crack detection. In this paper, we improve the EfficientNetB0 to realize the detection of concrete surface cracks using the transfer learning method. The model is designed by neural architecture search technology. The weights are pretrained on the ImageNet. Supervised learning uses Adam optimizer to update network parameters. In the testing process, crack images from different locations were used to further test the generalization capability of the model. By comparing the detection results with the MobileNetV2, DenseNet201, and InceptionV3 models, the results show that our model greatly reduces the number of parameters while achieving high accuracy (0.9911) and has good generalization capability. Our model is an efficient detection model, which provides a new option for crack detection in areas with limited computing resources.
Highlights
In the current infrastructure, the concrete structure accounts for the largest proportion
With the increase in service time, the number and width of cracks show a gradual increasing trend, which seriously affects the safety, applicability, and durability of the structure. erefore, it is of great significance to detect cracks regularly and takes corresponding maintenance measures for the safety of the concrete structures [1,2,3]. e traditional crack detection method is mainly based on the direct detection of professionals with related instruments. is detection method is labor-consuming and time wasting
In order to find an efficient and safe crack detection method and overcome various shortcomings of manual detection, people turned their attention to image processing technologies (IPTs) [4]
Summary
The concrete structure accounts for the largest proportion. CNN is a feed-forward neural network, and the connection between neurons is inspired by the animal visual cortex It has the characteristics of local connectivity and parameter sharing and has excellent performance in large-scale image processing [14]. An end-to-end object detection method based on deep learning was used to train the damage detection model. Tong et al [20] used convolutional neural networks (CNNs) to detect, locate, and measure ground penetrating radar images automatically and reconstruct concealed cracks in three dimensions, realizing a low-cost damage characterization method. Yang et al [21] realized the pixel-level detection of cracks based on a fully convolutional neural network. We use the transfer learning method to build a model for concrete surface crack detection. E remaining of this paper is structured as follows: Section 2 describes the dataset and image preprocessing method; Section 3 presents the overall model architecture and training details; Section 4 shows our experimental results; and Section 5 delivers the conclusion of this paper
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