Steel plate surface defect detection method based on multistage characteristics of convolutional neural network
A convolutional neural network and defect detection technology, which is applied in the field of steel surface defect detection based on multi-level features of convolutional neural network, can solve problems such as insufficient classification ability, lack of corresponding data, and inability to obtain accurate defect locations.
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[0062] The specific embodiments of the present invention will be described in further detail below in conjunction with the drawings and embodiments. The following examples are used to illustrate the present invention, but not to limit the scope of the present invention.
[0063] Surface defect detection method of steel plate based on multi-level features of convolutional neural network, such as figure 1 As shown, including the following steps:
[0064] Step 1. Select an appropriate reference network, and then use the large data set ImageNet to pre-train the reference network;
[0065] The reference network selects highly modular residual networks ResNet34 and ResNet50. Both residual networks include the first convolutional layer conv1, four residual modules {R2, R3, R4, R5} and subsequent global maximum pooling Layer and classification output layer; the difference between the two residual networks is that the number of convolutional layers and the number of convolution kernels in t...
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