Crack identifying method based on deep learning convolutional neural network
A convolutional neural network and deep learning technology, applied in biological neural network models, neural architecture, character and pattern recognition, etc., can solve problems such as errors and a large number of sensors, improve efficiency, reduce image quality requirements, reduce labor The effect of workload
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[0032] The following combination figure 1 The target image acquisition schematic shown in and figure 2 The implementation flow chart shown in, takes a bridge as an example (actually applicable to various structures), and further sets forth the specific embodiment of the present invention.
[0033] illustration: figure 1 The codes in represent respectively:
[0034] 1 - target structure;
[0035] 2——Cracks on the surface of the target structure;
[0036] 3 - No cracks on the surface of the target structure;
[0037] 4——There is a crack area on the surface of the target structure;
[0038] 5 - digital camera;
[0039] Remarks: The images collected in the present invention should include images collected under various actual conditions such as different light and shade and light intensity.
[0040] A method for identifying cracks based on a deep convolutional neural network according to the present invention, the specific steps are as follows:
[0041] A. Collect images ...
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