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DOE OSTI · 3023577

Developing a Deep Learning-Computer Vision Framework to Monitor Avian Interactions with Solar Energy Facility Infrastructure (Final Technical Report)

Hamada, Yuki [Argonne National Laboratory (ANL), Argonne, IL (United States)] (ORCID:0000000316832509)·Szymanski, Adam Z. [Argonne National Laboratory (ANL), Argonne, IL (United States)]·Tarpey, Paul F. [Argonne National Laboratory (ANL), Argonne, IL (United States)]
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Abstract

The project addressed an inability to monitor avian interactions with photovoltaic (PV) solar energy facilities necessary for understanding PV solar impacts on birds. In the project, machine-vision technology that continuously monitors avian activities at PV solar facilities was developed. The technology includes four machine-learning (ML) models, each of which accomplishes a specific task in detecting birds and classifying their activities in live or recorded videos—detecting and tracking moving objects, differentiating birds from other objects, detecting bird collisions with solar panels, and classifying non-collision bird activities around PV facilities. Major project outcomes include adoption by two of DOE SETO’s SolWEB projects, providing novel observational data on birds to promote co-location of PV solar development and habitat conservation, known as ecovoltaics.

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14 SOLAR ENERGY59 BASIC BIOLOGICAL SCIENCES97 MATHEMATICS AND COMPUTINGArtificial intelligenceAvian mortalityBiological movementBird behaviorCollisionEcologyEcovoltaicFatalityLake effectMachine learningUtility scale solar

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Hamada, Yuki [Argonne National Laboratory (ANL), Argonne, IL (United States)] (ORCID:0000000316832509), Szymanski, Adam Z. [Argonne National Laboratory (ANL), Argonne, IL (United States)], Tarpey, Paul F. [Argonne National Laboratory (ANL), Argonne, IL (United States)]. 2026-05-06. Developing a Deep Learning-Computer Vision Framework to Monitor Avian Interactions with Solar Energy Facility Infrastructure (Final Technical Report). https://doi.org/10.2172/3023577

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