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- W4387444298 abstract "As one of the indispensable materials in modern industry, high precision and high efficiency defect detection of strip weld has become an important part of production and quality control. In response to the shortcomings of the existing strip weld detection technology with low detection accuracy, this paper proposes a deep learning based strip weld defect detection and classification technique. First, the CBAM attention mechanism is introduced into the network structure to improve the attention to important features; second, the traditional convolution layer is replaced by using the dilated convolution to reduce the computation while increasing the perceptual field; finally, the original ReLU activation function is replaced by the PReLU activation function to effectively overcome the problem of neuron death in the Relu activation function. The improved model finally achieves a recognition rate of 98.1% in the test set, which is 22.1% higher than that of the unimproved model, greatly improving the accuracy and robustness of the algorithm." @default.
- W4387444298 created "2023-10-10" @default.
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- W4387444298 date "2023-07-08" @default.
- W4387444298 modified "2023-10-11" @default.
- W4387444298 title "Strip Weld Inspection Based on Improved VGG16" @default.
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- W4387444298 doi "https://doi.org/10.1109/icsip57908.2023.10270895" @default.
- W4387444298 hasPublicationYear "2023" @default.
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