Research on image detection and recognition for defects on the surface of the steel plate based on magnetic flux leakage signals

In order to improve CCD recognition accuracy for defection on the steel plate surface caused by illumination uniformity of light source, light color, impact of site environmental and the low signal-to-noise ratio images on the surface of the steel plate collected by the system, this paper proposes a new image process and BP neural network detection recognization method based on magnetic flux leakage data in order to realize accurate and effective identification on the surface of steel plate. In this paper, non-destructive magnetic flux leakage detection technology is used to replace the CCD detection method to collect data. And then we use the image conversion technology to convert the data into images. After that, the image processing technology is used to detect the defects and extract the features. Finally, the BP neural network defect identification model is constructed to identify the defects. The simulation results show that the trained model has a strong ability to recognize length, width and depth of the defects. The new method can effectively detect and identify low-contrast and small defects in the weak signal, which can furtherly improve the detection resolution and sensitivity.