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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.18136 |
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| _version_ | 1866915462120472576 |
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| author | Klar, Nico Gifary, Nizam Ziegler, Felix P. G. Sehnke, Frank Kaifel, Anton Price, Eric Ahmad, Aamir |
| author_facet | Klar, Nico Gifary, Nizam Ziegler, Felix P. G. Sehnke, Frank Kaifel, Anton Price, Eric Ahmad, Aamir |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_18136 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BirdRecorder's AI on Sky: Safeguarding birds of prey by detection and classification of tiny objects around wind turbines Klar, Nico Gifary, Nizam Ziegler, Felix P. G. Sehnke, Frank Kaifel, Anton Price, Eric Ahmad, Aamir Computer Vision and Pattern Recognition Machine Learning Robotics Systems and Control 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. |
| title | BirdRecorder's AI on Sky: Safeguarding birds of prey by detection and classification of tiny objects around wind turbines |
| topic | Computer Vision and Pattern Recognition Machine Learning Robotics Systems and Control |
| url | https://arxiv.org/abs/2508.18136 |