# Approach for Tattoo Detection and Identification Based on YOLOv5 and Similarity Distance
## 1 Introduction  
The rapid growth of digital images that could be retrieved from different sources has created a strong demand for efficient object localization and detection methods in various fields, such as medicine, military, manufacturing, law enforcement, and forensics. There is much research in the scientific literature that focuses on the detection of different diseases by analyzing images using various object detection or localization algorithms. For example, the research by [PERSON] et al. [1] and [PERSON] et al. [2] focuses on the analysis of the image of medicine in which the images of glaucoma and prostate cancer images have been analyzed. In the research of [PERSON] et al. [3], the image data obtained from an uncrewed aerial vehicle were analyzed. The main aim of the research was to detect military vehicles. The Industry 4.0 context has led to many intelligences or automated solutions based on object detection as well. The research by [PERSON] et al. [4] used deep learning algorithms to detect product defects in manufacturing. The other research by [PERSON] et al. [5] analyzed metal surface defects using object detection methods, where theresults are useful and can be applied to improve manufacturing lines. Object detection and localization tasks are very popular and could be applied in different solutions, for example, for the detection of construction details [6,7] or as a component of travel direction recommendation systems [8]. In law enforcement and forensics, different types of hard biometric data can be used to identify people in the image, such as the iris pattern, facial features, or fingerprint. Despite significant advances in hard biometric identification, a single biometric characteristic cannot guarantee the desired identification accuracy. Characteristics of people who are less unique compared to traditional biometric data are called soft biometric data. As one of the soft biometric features, tattoos are valuable in helping people identify associations, groups, members, gangs, criminals, or victims. Tattoos are considered soft biometrics because, over time, the tattoo on the human body may change, compared to hard biometric characteristics such as fingerprints or iris [9]. However, the accuracy of automatic tattoo identification and detection is challenged by a wide range of artistic compositions, colors, shapes, textures, image conditions, and quality [10]. Therefore, it is more difficult to choose the right model to solve this problem. The concept of tattoo detection and identification using deep learning involves the use of machine learning techniques, specifically deep learning, to identify and locate tattoos on the human body, as well as object detection methods. This is a relatively new area of research, as tattoos have traditionally been difficult to analyze and classify using automated techniques due to their complex and highly variable visual appearance. In the context of tattoo detection and recognition, deep learning techniques can be used to analyze images of the human body and identify the presence and location of tattoos. This can be useful in a variety of applications, including law enforcement, forensic analysis, and medical research. In general, the use of deep learning for tattoo detection and recognition represents a significant advance in the field of machine learning and has the potential to revolutionize the way tattoos are analyzed and understood. The main aim of this paper is to detect tattoos on a person's body and then link them with the data available in the database to identify to whom it belongs. An experimental investigation has been performed to find out the influence of various hyperparameters of YOLOv5, data augmentation, and similarity distances on tattoo detection and identification. The main contributions of the paper are as follows:  
1. A total of 135 models have been trained to detect which model of YOLOv5 (n, s, m, l, x) allows obtaining the highest results in tattoo detection.