Saving Birds from Power Grids: A Deep Learning Embedded System for Sustainable Energy Infrastructure

Energy sustainability remains one of the humanity’s most pressing challenges. Electrical power systems, while essential for modern infrastructure, pose significant risks to wildlife, particularly birds. When birds approach to high-voltage power grids, they face fatal electrocution, leading to ecological harm and potential damage to electrical infrastructure. To mitigate this issue, we propose an embedded system that combines low-cost hardware with deep learning for real-time avian detection and deterrence. Our solution employs the YOLO-v۲ neural network, implemented on a K۲۱۰ embedded processor featuring dual ۶۴-bit cores running at ۴۰۰ MHz, achieving an inference speed of ۴۵ frames per second. The model was trained on a curated dataset of ۱,۵۰۰ diverse bird images, optimized for network input, and achieved an accuracy of ۸۹% during validation. This system demonstrates a practical, efficient, and scalable approach to reducing avian fatalities near power grids while maintaining grid reliability.

Keywords:

High-voltage transmission lines , Convolutional Neural Networks , Bird Detection , YOLO-v۲ Network , Edge Computing