Evaluation Method of Metal Corrosion Grade Based on Image Processing

Metals inevitably undergo corrosion on its surface during service. The current metal corrosion grade evaluation in the engineering is mainly based on manual evaluation, which has the problems of low efficiency and poor accuracy. According to the differentiation characteristics of pixels before and after metal corrosion, convolutional neural network combined with sliding window method was used to achieve corrosion feature classification and corrosion area location, and a method of color clustering combined with standard color map information table was proposed to achieve metal corrosion grade evaluation by computer. The results show that the evaluation accuracy rate of this method reached 96%, which had the advantages of fast detection speed, strong objectivity and high accuracy, and solved the problem of rapid evaluation of metal corrosion grades based on multiple indexes.