Real-Time Object Detection for Autonomous Solar Farm Inspection via UAVs
SENSORS(2024)
摘要
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 x1376 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.
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关键词
keypoint detection,onboard processing,embedded platforms,active learning,autonomous navigation,neural networks,uncertainty estimation
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