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

Qiaoyue Man,Youngim Cho,Lintong Zhang

doi:10.3390/app11167333

Abstract

With increasing interest in hairstyles and hair color, bleaching, dyeing, straightening, and curling hair is being widely used worldwide, and the chemical and physical treatment of hair is also increasing. As a result, hair has suffered a lot of damage, and the degree of damage to hair has been 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 reversing hair damage. Manual observation methods cannot accurately and quickly identify hair damage areas. In recent years, 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, found the relationship between them, used a convolutional neural network to recognize and confirm the degree of hair damage, and categorized the degree of damage into weak damage, moderate damage and high damage.

Highlights

With increasing interest in hairstyles and hair color, bleaching, dyeing, straightening, and curling hair is being widely used worldwide, and the chemical and physical treatment of hair is increasing
We proposed a novel and effective convolutional network model for hair damage detection: RCSAN-Net
We created a system based on artificial intelligence convolutional neural network that could care and maintenance, medical diagnosis directions

Summary

Introduction

With increasing interest in hairstyles and hair color, bleaching, dyeing, straightening, and curling hair is being widely used worldwide, and the chemical and physical treatment of hair is increasing. Hair has suffered a lot of damage, and the degree of damage to hair has been measured only by the naked eye or touch. This has led to serious consequences, such as hair damage and scalp diseases. The normal cuticle has a smooth appearance, reflecting light and limiting friction between hair shafts. It is responsible for the shine and texture of hair. Most people’s hair is prone to various damage problems because hair is inconvenient to observe, it is impossible to conduct a detailed analysis, and there are few studies related to hair damage. Microscopic analysis can be used as a tool to assess hair damage, as an indicator of health status, by identifying the morphological characteristics of hair damage, and it can be analyzed in a qualitative manner

Methods

Results

Discussion

Conclusion