Efficient Hair Damage Detection Using SEM Images Based on Convolutional Neural Network

Abstract

With increasing interest in hairstyles and hair color, bleaching, dyeing, straightening, and curling hair are widely used worldwide, and the chemical and physical treatment of hair is also increasing. As a result, the hair suffered a lot of damage, and the degree of damage to the hair was measured only by the naked eye or touch. This has led to serious consequences, such as hair damage and scalp diseases. However, although these problems are serious, there is little research on hair damage. With the advancement of technology, people began to be interested in preventing and restoring hair damage. Manual observation methods cannot accurately and quickly identify hair damage areas. With the rise of artificial intelligence technology, a large number of applications in various scenarios have given researchers new methods. In the project, we created a new hair damage data set based on SEM (Scanning Electron Microscope) images. Through various physical and chemical analyses, we observe the changes in the hair surface according to the degree of hair damage, find the relationship between them, and use intelligence the convolutional neural network recognizes and confirms the degree of hair damage, and divides the degree of damage into weak damage, damage and extreme damage.