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

Javier Rodriguez-Vazquez1,2,3, Inés Prieto-Centeno1,2,3, Miguel Fernandez-Cortizas1,2

  • 1Computer Vision and Aerial Robotics Group, Universidad Politécnica de Madrid (CVAR-UPM), 28040 Madrid, Spain.

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Summary
This summary is machine-generated.

This study presents a keypoint-based object detection framework for real-time solar farm inspections using unmanned aerial vehicles (UAVs). The method enhances detection precision and operational efficiency for robotic missions.

Keywords:
active learningautonomous navigationembedded platformskeypoint detectionneural networksonboard processinguncertainty estimation

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Solar farm inspection requires agile and precise object detection.
  • Conventional methods like bounding boxes or segmentation lack granularity for detailed inspection.
  • Unmanned aerial vehicles (UAVs) are increasingly used for industrial asset inspection.

Purpose of the Study:

  • To introduce an innovative keypoint-based object detection framework for real-time solar farm inspections.
  • To improve the granularity of object detection by focusing on solar panel vertices.
  • To optimize the framework for embedded platforms for efficient robotic operations.

Main Methods:

  • Developed a keypoint-based object detection framework inspired by CenterNet.
  • Optimized the architecture for embedded platforms (e.g., NVIDIA AGX Jetson Orin).
  • Integrated active learning strategies to reduce annotation efforts.

Main Results:

  • Achieved near 60 FPS at 1024 ×1376 resolution, exceeding camera operational frequency.
  • Demonstrated a real-time capability essential for time-critical industrial inspections.
  • Model design emphasizes reduced computational demand for practical deployment.

Conclusions:

  • Keypoint-based object detection offers a practical and effective approach for UAV-based solar farm inspections.
  • The proposed framework provides richer granularity compared to traditional methods.
  • The system is optimized for real-time performance and reduced computational load.