Moreover, image stitching based on Oriented FAST and Rotated BRIEF feature matching algorithm is realized, in order to overcome the limitation of camera field of view. 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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