CNN-deep-learning-based DBCC classification model and constructing method thereof
A classification model and deep learning technology, applied in image analysis, character and pattern recognition, image data processing, etc., can solve problems such as inaccurate recognition of small pictures and poor accuracy of crack recognition
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Embodiment 1
[0035] refer to Figure 1-Figure 4 , a method for building a DBCC classification model based on CNN deep learning, comprising the following steps:
[0036] (1) Convolute and sum the input original picture with all the convolution kernels in the first convolutional layer in a convolutional manner to obtain the feature map of the first convolutional layer;
[0037] (2) Add a Relu activation function after the first convolutional layer;
[0038] (3) After the first convolutional layer, add a local response value normalization layer for picture brightness correction, and the local response value normalization layer improves the recognition effect of the network;
[0039] (4) Downsample the feature map of the first convolutional layer in the first pooling layer, reduce the resolution and select excellent features as the feature map of the first pooling layer;
[0040] (5) On the second convolutional layer, the feature map of the first pooling layer and all the convolution kernels...
Embodiment 2
[0076] refer to Figure 5 , describe in detail the operation process of bridge crack detection and location:
[0077] The first step is to use image acquisition equipment to collect five bridge crack pictures with different background textures and materials. The total number of collected pictures is 2000, and all pictures are normalized into 1024*1024 resolution pictures. The pictures are divided into 2 data sets, artificially amplified data set and test data set, each data set has 1000 pictures;
[0078] In the second step, use a W*H fixed-size window to slide non-overlapping 1000 pictures in the artificial amplification data set, and at the same time, use the small slice of the bridge crack picture covered by the sliding window as a ROI region of interest. Among them, the small slice image containing the bridge background is called the bridge background surfel, and the small slice containing the bridge crack is called the bridge crack surfel. The specific process is shown i...
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