Bilgisayarlı Görme ve Derin Öğrenme Yöntemleri Kullanılarak Balon Balığının Tespiti

Abstract

Süveyş Kanalı'nın açılması ve Aswan Barajı'nın inşası, Akdeniz ekosistemini önemli ölçüde etkilemiştir. Bu değişiklikler, Kızıldeniz'den Akdeniz'e türlerin göçünü artırarak yeni türlerin yayılmasına yol açmış, ekonomik kayıplara ve insan sağlığına yönelik tehditlere neden olmuştur. Bu türler arasında balon balığı, doğal avcısı olmayan ve geniş alanlara yayılan toksik bir türdür. Bu çalışma, bilgisayarlı görüş ve derin öğrenme teknikleri kullanılarak balon balığının (Lagocephalus sceleratus) tespiti için bir nesne tanıma modeli eğitilmesine odaklanmaktadır. En gelişmiş algoritmalardan biri olan YOLO (You Only Look Once) kullanılmıştır. Modelin eğitimi için veriler, Akdeniz'deki dalış okulları ve eğitmenlerden toplanmıştır. Sualtı videolarından çıkarılan kareler etiketlenerek 2473 görüntüden oluşan bir veri seti oluşturulmuştur. YOLOv8m sürümü, %96,90 mAP değeri ile en iyi sonucu elde etmiştir. Modelin, balon balığını baş ve yan açılardan daha iyi tespit ettiği görülmüştür. Ancak, manuel etiketleme sürecinde yaşanan zorluklar, özellikle balığın kuyruk ve yüzgeçlerinde, modelin odaklanmasını bir miktar etkilemiştir. Bu çalışmanın bulguları, su altı robotları ve otomatik sistemlerle balon balığı popülasyonunun kontrol altına alınmasına yardımcı olabilir ve ekolojik dengeye katkı sağlayabilir. Akdeniz Üniversitesi'nde birinci yazarın aynı başlıklı yüksek lisans tezinden üretilen bu çalışma, gelecekteki sürdürülebilir çözümler için bir temel sunmaktadır. Bu çalışma, birinci yazarın Akdeniz Üniversitesi, Yönetim Bilişim Sistemleri Anabilim Dalı'nda hazırladığı aynı başlıklı yüksek lisans tezinden üretilmiştir.

Keywords

• Bilgi Sistemleri
• Derin Öğrenme
• Bilgisayarlı Görü
• YOLO
• Balon Balığı
• Nesne Tespit

Detection Of Pufferfish Using Computer Vision And Deep Learning Methods

Abstract

The opening of the Suez Canal and the construction of the Aswan Dam have significantly impacted the Mediterranean ecosystem. These changes increased species migration from the Red Sea to the Mediterranean, leading to the spread of new species, causing economic losses and threats to human health. Among these, the pufferfish is a toxic species with no natural predators and wide distribution. This study focuses on training an object detection model to identify pufferfish (Lagocephalus sceleratus) using computer vision and deep learning techniques. YOLO (You Only Look Once), a leading algorithm, was used. Training data were gathered from diving schools and instructors in the Mediterranean. Frames extracted from underwater videos were labeled to create a dataset of 2,473 images. The YOLOv8m version achieved the best result with a mAP of 96.90%. The model was better at detecting pufferfish from head and side angles. However, challenges in manual labeling, particularly with tails and fins, slightly affected the model’s focus. This study’s findings could help control pufferfish populations using underwater robots and automated systems, contributing to ecological balance. Derived from the first author's master's thesis at Akdeniz University, it offers a foundation for future sustainable solutions.

Keywords

• Information Systems
• Deep Learning
• Computer Vision
• YOLO
• Pufferfish
• Object Detection

Ethical Statement

This study received ethical approval from the Scientific Research and Publication Ethics Committee of Akdeniz University, with the decision dated September 5, 2023, under reference number 370.

Thanks

We would like to extend our gratitude to Professor Dr. Mehmet Gökoğlu, Associate Professor Dr. Ahmet Balcı, Star Diving Academy, Posseidon Kemer Diving, and Diving Instructor Orkun Tekin for their support during this study.

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