Performance Analysis of Different Types of NN Models for Target Recognition

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dc.citation.epage107
dc.citation.issue2
dc.citation.journalTitleДосягнення у кіберфізичних системах
dc.citation.spage101
dc.citation.volume9
dc.contributor.affiliationLviv Polytechnic National University
dc.contributor.authorTsiunyk, Bohdan
dc.contributor.authorMuliarevych, Oleksandr
dc.coverage.placenameЛьвів
dc.coverage.placenameLviv
dc.date.accessioned2025-11-06T08:48:10Z
dc.date.created2024-02-27
dc.date.issued2024-02-27
dc.description.abstractThe objective of this research is to conduct a comprehensive performance analysis of various types of neural network (NN) models for target recognition. Specifically, this study focuses on evaluating the effectiveness and efficiency of yolov8n, yolov8s, yolov8m models in target recognition tasks. Leveraging cutting-edge technologies such as OpenCV, the research is aimed at developing a robust methodology for assessing the performance of these NN models. Through meticulous analysis, this study aims to provide insights into the strengths and weaknesses of each model, facilitating informed decision-making for practical applications1. This paper presents the process of designing and conducting the performance analysis. The study discusses the implications of the findings for future developments in target recognition systems.
dc.format.extent101-107
dc.format.pages7
dc.identifier.citationTsiunyk B. Performance Analysis of Different Types of NN Models for Target Recognition / Bohdan Tsiunyk, Oleksandr Muliarevych // Advances in Cyber-Physical Systems. — Lviv : Lviv Politechnic Publishing House, 2024. — Vol 9. — No 2. — P. 101–107.
dc.identifier.citationenTsiunyk B. Performance Analysis of Different Types of NN Models for Target Recognition / Bohdan Tsiunyk, Oleksandr Muliarevych // Advances in Cyber-Physical Systems. — Lviv : Lviv Politechnic Publishing House, 2024. — Vol 9. — No 2. — P. 101–107.
dc.identifier.doidoi.org/10.23939/acps2024.02.101
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/117382
dc.language.isoen
dc.publisherВидавництво Львівської політехніки
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofДосягнення у кіберфізичних системах, 2 (9), 2024
dc.relation.ispartofAdvances in Cyber-Physical Systems, 2 (9), 2024
dc.relation.references[1] Das, S., Saha, S., Coello Coello, C.A., Bansal, J.C., (2023). «Deep Neural Network Based Performance Evaluation and Comparative Analysis of Human Detection in Crowded Images Using YOLO Models». In International Conference on Advances in Data-Driven Computing and Intelligent Systems. ADCIS. Lecture Notes in Networks and Systems, vol 893. Springer, Singapore., pp. 508-509. DOI: https://doi.org/10.1007/978-981-99-9518-9_37
dc.relation.references[2] Delleji, T., Slimeni, F., Fekih, H. (2022). “An UpgradedYOLO with Object Augmentation: Mini-UAV Detection Under Low-Visibility Conditions by Improving Deep Neural Networks”. Oper. Res. Forum 3, 60, pp. 3–5. DOI: 10.1007/s43069-022-00163-7.
dc.relation.references[3] Tattari, J., Donthi, V. R., Mukirala, D., Komar Kour, S. (2021). “Deep Neural Networks Based Object Detection or Road Safety Using YOLO-V3”. In Smart Computing Techniques and Applications. Smart Innovation, System and Technologies, vol. 225. Springer, Singapore, pp. 731–733. DOI: 10.1007/978-981-16-0878-0_71.
dc.relation.references[4] Poskart, B., Iskierka, G., Krot, K. (2024). “Logistics 4.0 – Monitoring of Transport Trolley in the Factory Through Vision Systems Using the YOLO Model Based on Convolution Neural Networks”, In International Conference on Intelligent Systems in Production Engineering and Maintenance III. ISPEM 2023. Lecture Notes in Mechanical Engineering, Springer, Cham., pp. 348–350. DOI: 10.1007/978-3-031-44282-7_27.
dc.relation.references[5] Priyankan, K. and Fernando, T. G. I. (2021). “Mobile Application to Identify Fish Species Using YOLO and Convolutional Neural Networks.” In Proceedings of International Conference on Sustainable Expert Systems: ICSES 2020, volume 176. Springer, Singapore, pp. 304–308. DOI: 10.1007/978-981-33-4355-9_24.
dc.relation.references[6] Ayob, A. F., Khairuddin, K., Mustafah, Y. M., Salisa, A. R., Kadir, K. (2021). “Analysis of Pruned Neural Networks (MobileNetV2-YOLOv2) for Underwater Object Detection”. In: Proceedings of the 11th National Technical Seminar on Unmanned System Technology 2019. NUSYS 2019. Lecture Notes in Electrical Engineering, vol. 666. Springer, Singapore, pp. 87–88. DOI: 10.1007/978-981-15-5281-6_7.
dc.relation.references[7] A Byte of Python (2022). [Electronic resource]. Available at: https://homepages.uc.edu/~becktl/byte_of_python.pdf. (Accessed: March 29, 2024).
dc.relation.references[8] Bansal, J. C. and Uddin, M. S. (2023). “Computer Vision and Machine Learning in Agriculture, Vol. 3”. In: Algorithms for Intelligent Systems, pp. 120–125. DOI: 10.1007/978-981-99-3754-7.
dc.relation.references[9] Learning OpenCV (2022). [Electronic resource]. Available at: https://www.bogotobogo.com/cplusplus/files/OReilly%20Learning%20OpenCV.pdf. (Accessed: March 29, 2024).
dc.relation.references[10] Huang, D. S., Premaratne, P., Jin, B., Qu, B., Jo, K. H. and Hussain, A. (2023). “Advanced Intelligent Computing Technology and Application”. Springer, Singapore, pp. 83–88. DOI: 10.1007/978-981-99-4742-3
dc.relation.references[11] Lys, R., Opotyak, Y. (2023). “Development of a Video Surveillance System for Motion Detection and Object Recognition”. Advances in Cyber-Physical Systems, 8(1), pp. 50–53. DOI: 10.23939/acps2023.01.050.
dc.relation.referencesen[1] Das, S., Saha, S., Coello Coello, C.A., Bansal, J.C., (2023). "Deep Neural Network Based Performance Evaluation and Comparative Analysis of Human Detection in Crowded Images Using YOLO Models". In International Conference on Advances in Data-Driven Computing and Intelligent Systems. ADCIS. Lecture Notes in Networks and Systems, vol 893. Springer, Singapore., pp. 508-509. DOI: https://doi.org/10.1007/978-981-99-9518-9_37
dc.relation.referencesen[2] Delleji, T., Slimeni, F., Fekih, H. (2022). "An UpgradedYOLO with Object Augmentation: Mini-UAV Detection Under Low-Visibility Conditions by Improving Deep Neural Networks". Oper. Res. Forum 3, 60, pp. 3–5. DOI: 10.1007/s43069-022-00163-7.
dc.relation.referencesen[3] Tattari, J., Donthi, V. R., Mukirala, D., Komar Kour, S. (2021). "Deep Neural Networks Based Object Detection or Road Safety Using YOLO-V3". In Smart Computing Techniques and Applications. Smart Innovation, System and Technologies, vol. 225. Springer, Singapore, pp. 731–733. DOI: 10.1007/978-981-16-0878-0_71.
dc.relation.referencesen[4] Poskart, B., Iskierka, G., Krot, K. (2024). "Logistics 4.0 – Monitoring of Transport Trolley in the Factory Through Vision Systems Using the YOLO Model Based on Convolution Neural Networks", In International Conference on Intelligent Systems in Production Engineering and Maintenance III. ISPEM 2023. Lecture Notes in Mechanical Engineering, Springer, Cham., pp. 348–350. DOI: 10.1007/978-3-031-44282-7_27.
dc.relation.referencesen[5] Priyankan, K. and Fernando, T. G. I. (2021). "Mobile Application to Identify Fish Species Using YOLO and Convolutional Neural Networks." In Proceedings of International Conference on Sustainable Expert Systems: ICSES 2020, volume 176. Springer, Singapore, pp. 304–308. DOI: 10.1007/978-981-33-4355-9_24.
dc.relation.referencesen[6] Ayob, A. F., Khairuddin, K., Mustafah, Y. M., Salisa, A. R., Kadir, K. (2021). "Analysis of Pruned Neural Networks (MobileNetV2-YOLOv2) for Underwater Object Detection". In: Proceedings of the 11th National Technical Seminar on Unmanned System Technology 2019. NUSYS 2019. Lecture Notes in Electrical Engineering, vol. 666. Springer, Singapore, pp. 87–88. DOI: 10.1007/978-981-15-5281-6_7.
dc.relation.referencesen[7] A Byte of Python (2022). [Electronic resource]. Available at: https://homepages.uc.edu/~becktl/byte_of_python.pdf. (Accessed: March 29, 2024).
dc.relation.referencesen[8] Bansal, J. C. and Uddin, M. S. (2023). "Computer Vision and Machine Learning in Agriculture, Vol. 3". In: Algorithms for Intelligent Systems, pp. 120–125. DOI: 10.1007/978-981-99-3754-7.
dc.relation.referencesen[9] Learning OpenCV (2022). [Electronic resource]. Available at: https://www.bogotobogo.com/cplusplus/files/OReilly%20Learning%20OpenCV.pdf. (Accessed: March 29, 2024).
dc.relation.referencesen[10] Huang, D. S., Premaratne, P., Jin, B., Qu, B., Jo, K. H. and Hussain, A. (2023). "Advanced Intelligent Computing Technology and Application". Springer, Singapore, pp. 83–88. DOI: 10.1007/978-981-99-4742-3
dc.relation.referencesen[11] Lys, R., Opotyak, Y. (2023). "Development of a Video Surveillance System for Motion Detection and Object Recognition". Advances in Cyber-Physical Systems, 8(1), pp. 50–53. DOI: 10.23939/acps2023.01.050.
dc.relation.urihttps://doi.org/10.1007/978-981-99-9518-9_37
dc.relation.urihttps://homepages.uc.edu/~becktl/byte_of_python.pdf
dc.relation.urihttps://www.bogotobogo.com/cplusplus/files/OReilly%20Learning%20OpenCV.pdf
dc.rights.holder© Національний університет “Львівська політехніка”, 2024
dc.rights.holder© Tsiunyk B., Muliarevych O., 2024
dc.subjectyolov8
dc.subjectYOLO
dc.subjectOpenCV
dc.subjectNN model
dc.titlePerformance Analysis of Different Types of NN Models for Target Recognition
dc.typeArticle

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Advances In Cyber-Physical Systems. – 2024. – Vol. 9, No. 2