| Summary: | Aiming at the low efficiency and poor anti-interference ability of traditional non-destructive testing technology in steel plate crack detection, a crack recognition method based on convolutional neural network for infrared thermal imager is proposed. Firstly, a rolling electric heating rod is developed as a thermal excitation source, and a new excitation method was used to thermally excite the surface to be inspected. Then, according to the principle of abnormal temperature generated during the heat transfer process, the temperature of the detected surface is analyzed. It is concluded that the temperature gradient on both sides of the crack is always the largest. Finally, the infrared thermal image after thermal excitation is collected as a training sample, and a convolutional neural network is built to train the sample. Experiments show that the convolutional neural network model can accurately identify the infrared image cracks. The detection efficiency is high and the robustness is strong. And the recognition accuracy on the test set reaches 96.82%.
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