BirdRecorder's AI on Sky: Safeguarding birds of prey by detection and classification of tiny objects around wind turbines

2025-08-25

AI Summary

This study addresses the ecological conflict between wind energy expansion and avian collision risks—particularly for endangered raptors such as the red kite. To mitigate this, we propose an AI-driven real-time avian monitoring system. Methodologically, it integrates a lightweight Single Shot Detector (SSD) model, hardware-accelerated inference, and a multi-object tracking algorithm to achieve high-precision detection, species identification, and persistent tracking of small flying targets within an 800-meter range. Our key contribution lies in a software–hardware co-optimized architecture that significantly enhances both real-time performance and detection accuracy. Field evaluations demonstrate superior detection accuracy and response latency for red kites and other raptors under complex natural conditions, outperforming existing solutions. The system thus provides a deployable technical pathway supporting sustainable wind energy development while advancing biodiversity conservation.

Technology Category

• Machine Learning: Hardware-aware ML
• Computer Vision: Remote Sensing / Geospatial AI
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The urgent need for renewable energy expansion, particularly wind power, is hindered by conflicts with wildlife conservation. To address this, we developed BirdRecorder, an advanced AI-based anti-collision system to protect endangered birds, especially the red kite (Milvus milvus). Integrating robotics, telemetry, and high-performance AI algorithms, BirdRecorder aims to detect, track, and classify avian species within a range of 800 m to minimize bird-turbine collisions. BirdRecorder integrates advanced AI methods with optimized hardware and software architectures to enable real-time image processing. Leveraging Single Shot Detector (SSD) for detection, combined with specialized hardware acceleration and tracking algorithms, our system achieves high detection precision while maintaining the speed necessary for real-time decision-making. By combining these components, BirdRecorder outperforms existing approaches in both accuracy and efficiency. In this paper, we summarize results on field tests and performance of the BirdRecorder system. By bridging the gap between renewable energy expansion and wildlife conservation, BirdRecorder contributes to a more sustainable coexistence of technology and nature.

Problem

• Detecting and classifying tiny birds near wind turbines
• Preventing bird collisions with renewable energy infrastructure
• Balancing wind power expansion with wildlife conservation needs

Innovation

• AI-based real-time collision detection system
• Single Shot Detector for precise object recognition
• Hardware-accelerated tracking for efficient bird classification