用旋转目标检测自动绘制光伏板地图，提升运维效率
Solar Panel Mapping via Oriented Object Detection
- 采用旋转目标检测架构，精准定位倾斜的光伏板
- 在美国内多地数据集上达到83.3%的mAP指标
- 适合光伏电站智能巡检与自动化管理场景
维护光伏电站的完整性是应对当前气候危机的关键环节。该过程始于分析师绘制电站中每块光伏板的坐标地图，以便快速定位和修复故障面板。然而，这一任务极为繁琐，难以适应全球光伏装机容量的持续增长。为此，我们提出一种基于旋转目标检测架构的端到端深度学习框架，实现单个光伏板的自动检测。我们在覆盖美国多地的多样化光伏电站数据集上评估该方法，获得83.3%的mAP成绩。
原文摘要 · Abstract (English)
Maintaining the integrity of solar power plants is a vital component in dealing with the current climate crisis. This process begins with analysts creating a detailed map of a plant with the coordinates of every solar panel, making it possible to quickly locate and mitigate potential faulty solar panels. However, this task is extremely tedious and is not scalable for the ever increasing capacity of solar power across the globe. Therefore, we propose an end-to-end deep learning framework for detecting individual solar panels using a rotated object detection architecture. We evaluate our approach on a diverse dataset of solar power plants collected from across the United States and report a mAP score of 83.3%.
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