Abstract: Solar energy facilities are increasingly essential for renewable power generation, but their interaction with avian species pose ecological challenges, particularly bird collisions with solar panels. The Argonne National Laboratory (ANL) addresses the need for improved detection and classification of bird collisions using advanced machine learning techniques. This project seeks to improve upon the accuracy while maintaining computational efficiency for real-time monitoring of the existing model by leveraging object tracking, convolutional neural networks, and sequence-based models.
Keywords: bird collision detection, avian monitoring, machine learning, computer vision, solar energy, sequence modeling, CNNs, Argonne National Laboratory
Watch the team present this project in the session recording here.