![]() The accuracy of the final trained model on the test set can reach 0.985. Then, based on a transfer learning method, the pretrained GoogLeNet Inception V3 model is retrained by the crack dataset for better identifying the crack images. Firstly, a crack image dataset is acquired and constructed, which includes 2682 images with cracks and 983 images without crack at a resolution of 256 × 256 pixels. Therefore, this paper presents a crack detection technology based on a convolutional neural network, GoogLeNet Inception V3. ![]() ![]() Digital image processing techniques have great potential in automatically detecting cracks, which can replace the labor-intensive and highly subjective traditional manual on-site inspections. During the operating lifecycle of civil structures, cracks will occur inevitably, which may pose great threat to the safety of the structures without timely maintenance. ![]()
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