Sensors (Jan 2024)

Real-Time Object Detection for Autonomous Solar Farm Inspection via UAVs

  • Javier Rodriguez-Vazquez,
  • Inés Prieto-Centeno,
  • Miguel Fernandez-Cortizas,
  • David Perez-Saura,
  • Martin Molina,
  • Pascual Campoy

DOI
https://doi.org/10.3390/s24030777
Journal volume & issue
Vol. 24, no. 3
p. 777

Abstract

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Robotic missions for solar farm inspection demand agile and precise object detection strategies. This paper introduces an innovative keypoint-based object detection framework specifically designed for real-time solar farm inspections with UAVs. Moving away from conventional bounding box or segmentation methods, our technique focuses on detecting the vertices of solar panels, which provides a richer granularity than traditional approaches. Drawing inspiration from CenterNet, our architecture is optimized for embedded platforms like the NVIDIA AGX Jetson Orin, achieving close to 60 FPS at a resolution of 1024 ×1376 pixels, thus outperforming the camera’s operational frequency. Such a real-time capability is essential for efficient robotic operations in time-critical industrial asset inspection environments. The design of our model emphasizes reduced computational demand, positioning it as a practical solution for real-world deployment. Additionally, the integration of active learning strategies promises a considerable reduction in annotation efforts and strengthens the model’s operational feasibility. In summary, our research emphasizes the advantages of keypoint-based object detection, offering a practical and effective approach for real-time solar farm inspections with UAVs.

Keywords