# Approach for Tattoo Detection and Identification Based on YOLOv5 and Similarity Distance
## 2 Related Works
[17] and [PERSON] et al. [24] with support vector machine (SVM) and random forest classifiers for tattoo classification. Although these CBIR systems have been reported to provide quite high accuracy on various benchmarks, they require careful manipulation of characteristic descriptors, vocabulary sizes, and indexing algorithms. The success of deep learning has led to the point where CBIR's methods are shifting from handcrafted features and models to deep learning methods. The AlexNet method has been successfully used for tattoo vs. non-tattoo classification in the work of [PERSON] and [PERSON] [18]. [PERSON] et al. [10] also focused on the tasks of tattoo image detection and localization. The authors developed TATT-RBDL, a tattoo detector that can classify images with one or more tattoos. Then, the region-based deep learning method (Faster R-CNN) was applied to the domain-specific data, and a tattoo detector was trained using two datasets, one with tattoo images and one with non-tattoo images. [PERSON] and [PERSON] [22] presented another decision tree-based approach. It achieved only 53.38% accuracy in its own dataset, resulting in a less expressive result. Recently, [PERSON] et al. [20] have also presented a detection model using faster R-CNN. This model classified detection problems as examples of image recovery systems in which learning and detection were performed simultaneously. Another basic tattoo detection method [23] uses a GraphCut-based method, with an accuracy of 70.5%. [PERSON] and [PERSON] [9] presented a deep learning model based on transfer learning for tattoo detection problems. Due to the wide diversity of tattoo types and the lack of image capture standards, the datasets may be quite different. Therefore, it is difficult or even unreasonable to compare the results. Since the datasets are real-world samples, it is especially important for the efficiency of machine learning methods that the datasets that are used reflect the same diversity. In addition, for multiclass datasets, class samples are another prominent issue, as classifiers tend to be strongly biased. Unfortunately, many real-world datasets do not follow this principle. For example, the Tatt-C dataset presented in the previous table, which is widely used in the literature, consists of images of faces in non-tattoo classes. This may prejudice a classified trained with this dataset, which may acquire a false concept that images without tattoos are face-type images. Nevertheless, this database was the most used database for tattoo studies to date [9]. The first results on the Tatt-C dataset were published in response to the challenges issued by NIST [26]. According to the report by [PERSON] et al. [26], four institutions participated in this challenge, with MorphoTrek as the best performance, with 96.3% accuracy. Unfortunately, the algorithms developed by the participants in the NIST challenge have not been published. This was criticized by [PERSON] et al. [21] in their work, and it emerged that it was impossible to perform external validation tests. Although the concept of tattoo detection and identification is theoretically uncomplicated, the process is not simple and depends on various factors. There are no defined standards of what tattoos are in terms of shape, color, size, proportion of individuals, and their location on the body. Additionally, a single image may have several tattoos. Furthermore, the background of an image can introduce significant noise into the detection process because its complexity can be confused with the tattoo itself. Furthermore, it is difficult to compare different studies due to differences in the test procedures, metrics, and datasets used. There are relatively few publications dealing with tattoo detection and identification problems using deep learning. It should also be noted that most previous tattoo studies were based on the NIST Tatt-C dataset, which was discontinued over time and is no longer available for download and use.