# Efficient Hair Damage Detection Using SEM Images Based on Convolutional Neural Network
## 2 Related Work  
Hair micro-detail detection and recognition can find use in many disciplines such as medicine and forensics. In the context of forensic science, microscopic hair analysis (a qualitative method) has shown effective discrimination [3; 4], and detailed examination of hair under the microscope is helpful for forensic identification. Clinically, microscopic analysis can be used as a tool to assess hair damage, as an indicator of health status [5; 6], but the analysis needs to be performed under certain conditions. Hair is subjected to various physical and chemical agents, including detergents, dyes, combing, and ultraviolet rays, which will change the structure of the hair [7; 8; 9]. The morphological characteristics of  
Figure 1: Scanning electron microscope (SEM) imaging to observe hair magnification 800\(\times\) micrographs. damaged hair can be detected by qualitative methods for clinical research. Most studies focus on the identification of hair damage, and few studies quantify the degree of hair damage based on morphological characteristics [10; 11; 12]. It is worth noting that [PERSON] et al. proposed a classification system for hair damage. The system is divided into five damage levels, expounding the influence of external factors on hair damage [13]. After that, [PERSON] et al. extended it to 12 classifications [14]. Observing the images of hair from a scanning electron microscope (SEM), the researchers assessed the severity of hair structure irregularities subjectively and refined the degree of hair damage into 12 levels. In these studies, visual assessment was used as the main qualitative technique for hair damage. Few people have developed a more objective hair damage analysis and identification system, most of the research focuses on the manual analysis of morphological features detected by optical microscope and analysis using commercial software [15; 16], while there are few studies on automatic and efficient detection of hair damage. The detection and recognition of microscopic hair features is a challenging task. In recent decades, with the continuous development of microscopic examination technology, the microstructure of the hair that can be observed is clearer, and more precise, and the sample preparation is simple and high-resolution, which makes the study of the microscopic morphology of hair more in-depth. The manual detection method is still the most important practical detection method, but it usually takes at least one year to train qualified professional detection personnel. This is time-consuming and laborious because it requires the microscopist to observe the fiber morphology under the microscope for a long time, and the subjective and detection accuracy depends on the experience of the microscopist. For example, [PERSON] et al. [17], used SEM image data to study the relationship between hair damage that may be caused by repeated cosmetic treatments and the absorption of cocaine from a wettable solution into the hair matrix (simulating external contamination). Over the years, researchers have been developing a fast and accurate method to automatically detect the degree of hair damage and have made many useful attempts. These methods mainly include optical microscope and scanning electron microscope (SEM) observation analysis of microscopic hair images, and the application of image processing and computer vision technology to autonomously and efficiently detect the degree of hair damage. In recent years, with the emergence of convolutional neural networks and a large number of applications, Methods based on image processing and computer vision are the current research hotspot, greater number of researchers have focused on the field of hair detection. For example, [PERSON]. et al. [18] proposed a convolutional model that uses convolutional neural network algorithms from an unconstrained perspective and uses only texture information to achieve complete hair analysis (detection, segmentation, and hairstyle classification). [PERSON] et al.