A Deep Learning-Based Hybrid Detection Approach for Reducing Bird Collisions in Wind Turbines

Yıl 2025, Cilt: 8 Sayı: 2 , 226 - 233 , 12.12.2025

Uğur Cira , Abdulkadir Karacı , Adem Bayraktar

https://doi.org/10.51764/smutgd.1805320

https://izlik.org/JA47JT63XG

Öz

It is estimated that approximately 300,000 birds die annually worldwide due to collisions with wind turbines. In this study, an AI-assisted detection and intervention system was developed to minimize such collisions and enable wind turbines to operate without disrupting ecological balance. A dataset of 100 bird images under varying weather conditions and distances was collected, and the YOLOv4-tiny model was trained for 2000 epochs, achieving a mean average precision (mAP) of 95.8%. The trained model was deployed on an NVIDIA Jetson Nano and used with OpenCV-based image processing for real-time bird detection. When a bird is detected, an Arduino-controlled motor system stops the turbine blades, while an HC-SR04 ultrasonic sensor measures the bird’s distance to enhance decision stability. Testing under different lighting and distance conditions showed the best performance at high light and 30 cm (18 detections), and the lowest performance under low light and 42 cm (7 detections).

Anahtar Kelimeler

Wind turbine , bird safety , artificial intelligence , YOLO , autonomous systems

Rüzgâr Türbinlerinde Kuş Çarpışmalarının Azaltılmasına Yönelik Derin Öğrenme Tabanlı Hibrit Algılama Yaklaşımı

https://izlik.org/JA47JT63XG

Öz

Günümüzde rüzgar türbinleri nedeniyle yılda yaklaşık 300.000 kuşun yaşamını yitirdiği tahmin edilmektedir. Bu çalışmada, türbinlerin ekosisteme zarar vermeden çalışmasını sağlamak amacıyla yapay zekâ destekli bir kuş tespit ve müdahale sistemi tasarlanmıştır. Web üzerinden farklı hava koşulları ve uzaklıkları içeren 100 kuş görüntüsünden veri seti oluşturulmuş; YOLOv4-tiny modeli bu veriyle 2000 epok eğitilerek %95,8 mAP başarımı elde edilmiştir. Eğitilmiş model NVIDIA Jetson Nano üzerinde gerçek zamanlı olarak çalıştırılmış ve OpenCV tabanlı görüntü işleme ile kuş tespiti sağlanmıştır. Tespit durumunda Arduino kontrollü motor sistemi türbin pervanelerini durdurmakta, HC-SR04 sensörü ise kuş-türbin mesafesini ölçerek karar mekanizmasını desteklemektedir. Sistem, yüksek ışıkta ve 30 cm mesafede en iyi sonuçları (18 tespit), düşük ışıkta ve 42 cm mesafede ise en düşük performansı (7 tespit) göstermiştir.

Anahtar Kelimeler

Rüzgar türbini , kuş güvenliği , görüntü işleme , otonom sistemler , çevresel sürdürülebilirlik

Kaynakça

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