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Applied Sciences
Volume 14
Issue 13
10.3390/app14135576
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Open AccessArticle
by Gabija Pocevičė Gabija Pocevičė SciProfiles Scilit Preprints.org Google Scholar Pavel Stefanovič Pavel Stefanovič SciProfiles Scilit Preprints.org Google Scholar Simona Ramanauskaitė Simona Ramanauskaitė SciProfiles Scilit Preprints.org Google Scholar Ernest Pavlov Ernest Pavlov SciProfiles Scilit Preprints.org Google Scholar
1
Department of Information Systems, Vilnius Gediminas Technical University, Saulėtekio al. 11, LT-10223 Vilnius, Lithuania
2
Department of Information Technology, Vilnius Gediminas Technical University, Saulėtekio al. 11, LT-10223 Vilnius, Lithuania
3
Department of Electronic Systems, Vilnius Gediminas Technical University, Saulėtekio al. 11, LT-10223 Vilnius, Lithuania
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(13), 5576; https://doi.org/10.3390/app14135576
Submission received: 20 May 2024 / Revised: 11 June 2024 / Accepted: 26 June 2024 / Published: 26 June 2024
(This article belongs to the Special Issue Computer Vision in Automatic Detection and Identification)
Abstract
The large number of images in the different areas and the possibilities of technologies lead to various solutions in automatization using image data. In this paper, tattoo detection and identification were analyzed. The combination of YOLOv5 object detection methods and similarity measures was investigated. During the experimental research, various parameters have been investigated to determine the best combination of parameters for tattoo detection. In this case, the influence of data augmentation parameters, the size of the YOLOv5 models (n, s, m, l, x), and the three main hyperparameters of YOLOv5 were analyzed. Also, the efficiency of the most popular similarity distances cosine and Euclidean was analyzed in the tattoo identification process with the purpose of matching the detected tattoo with the person’s tattoo in the database. Experiments have been performed using the deMSI dataset, where images were manually labeled to be suitable for use by the YOLOv5 algorithm. To validate the results obtained, the newly collected tattoo dataset was used. The results have shown that the highest average accuracy of all tattoo detection experiments has been obtained using the YOLOv5l model, where [emailprotected]:0.95 is equal to 0.60, and [emailprotected] is equal to 0.79. The accuracy for tattoo identification reaches 0.98, and the F-score is up to 0.52 when the highest cosine similarity tattoo is associated. Meanwhile, to ensure that no suspects will be missed, the cosine similarity threshold value of 0.15 should be applied. Then, photos with higher similarity scores should be analyzed only. This would lead to a 1.0 recall and would reduce the manual tattoo comparison by 20%.
Keywords: YOLOv5; tattoo detection; data augmentation; hyperparameters; similarity distance; ResNet50
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MDPI and ACS Style
Pocevičė, G.; Stefanovič, P.; Ramanauskaitė, S.; Pavlov, E. Approach for Tattoo Detection and Identification Based on YOLOv5 and Similarity Distance. Appl. Sci. 2024, 14, 5576. https://doi.org/10.3390/app14135576
AMA Style
Pocevičė G, Stefanovič P, Ramanauskaitė S, Pavlov E. Approach for Tattoo Detection and Identification Based on YOLOv5 and Similarity Distance. Applied Sciences. 2024; 14(13):5576. https://doi.org/10.3390/app14135576
Chicago/Turabian Style
Pocevičė, Gabija, Pavel Stefanovič, Simona Ramanauskaitė, and Ernest Pavlov. 2024. "Approach for Tattoo Detection and Identification Based on YOLOv5 and Similarity Distance" Applied Sciences 14, no. 13: 5576. https://doi.org/10.3390/app14135576
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
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MDPI and ACS Style
Pocevičė, G.; Stefanovič, P.; Ramanauskaitė, S.; Pavlov, E. Approach for Tattoo Detection and Identification Based on YOLOv5 and Similarity Distance. Appl. Sci. 2024, 14, 5576. https://doi.org/10.3390/app14135576
AMA Style
Pocevičė G, Stefanovič P, Ramanauskaitė S, Pavlov E. Approach for Tattoo Detection and Identification Based on YOLOv5 and Similarity Distance. Applied Sciences. 2024; 14(13):5576. https://doi.org/10.3390/app14135576
Chicago/Turabian Style
Pocevičė, Gabija, Pavel Stefanovič, Simona Ramanauskaitė, and Ernest Pavlov. 2024. "Approach for Tattoo Detection and Identification Based on YOLOv5 and Similarity Distance" Applied Sciences 14, no. 13: 5576. https://doi.org/10.3390/app14135576
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
Appl. Sci., EISSN 2076-3417, Published by MDPI
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