Enhancing Aquarium Fish Classification through YOLO: A Deep Learning Approach
Yazarlar (2)
Dr. Öğr. Üyesi Murat IŞIK Kırşehir Ahi Evran Üniversitesi, Türkiye
Dr. Öğr. Üyesi Mehmet Ali YALÇINKAYA Kırşehir Ahi Evran Üniversitesi, Türkiye
| Bildiri Türü | Tebliğ/Bildiri | Bildiri Dili | İngilizce |
| Bildiri Alt Türü | Tam Metin Olarak Yayınlanan Tebliğ (Uluslararası Kongre/Sempozyum) | ||
| Bildiri Niteliği | Alanında Hakemli Uluslararası Kongre/Sempozyum | ||
| DOI Numarası | 10.1109/IDAP64064.2024.10710813 | ||
| Kongre Adı | 8th International Artificial Intelligence and Data Processing Symposium (IDAP'24) | ||
| Kongre Tarihi | 21-09-2024 / 22-09-2024 | ||
| Basıldığı Ülke | Türkiye | Basıldığı Şehir | Malatya |
| Bildiri Linki | https://ieeexplore.ieee.org/xpl/conhome/10710335/proceeding | ||
| Özet |
| In recent years, advancements in deep learning have revolutionized the field of image classification and object detection. This study presents a novel application of the YOLO (You Only Look Once) model for the classification of aquarium fish species. The model is designed to accurately identify and classify five distinct species: Percula Clownfish, Moorish Idol, Yellow Tang, Queen Angel Fish, and Blue Tang. Our approach leverages the YOLO v8 architecture due to its balance between speed and accuracy, making it suitable for real-time applications. The model was trained on a comprehensive dataset of annotated images collected manually from the internet, capturing various poses, lighting conditions, and background variations to enhance robustness. Experimental results demonstrate that the YOLO v8 model achieves high precision and recall rates, with an overall accuracy exceeding . The model’s … |
| Anahtar Kelimeler |
| artificial intelligence technologies | computer vision | YOLO |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| SCOPUS | 3 |