轻量级模型HOTSPOT-YOLO，用注意力机制提升无人机光伏热异常检测精度
HOTSPOT-YOLO: A Lightweight Deep Learning Attention-Driven Model for Detecting Thermal Anomalies in Drone-Based Solar Photovoltaic Inspections
- 融合轻量卷积网络与注意力机制，专为小而隐的热斑设计
- 平均精度达90.8%，显著优于传统检测模型
- 计算负担低，适合大规模光伏巡检，适合能源运维人员
太阳能光伏（PV）系统中的热异常检测对保障运行效率和降低维护成本至关重要。本文提出并命名了HOTSPOT-YOLO，一种轻量级人工智能模型，结合高效的卷积神经网络主干与注意力机制，以提升目标检测性能。该模型专为无人机热成像巡检光伏系统而设计，解决了检测微小、细微热异常（如热点和故障组件）的难题，同时保持实时性。实验结果表明，其平均精度达到90.8%，显著优于基线检测模型。模型计算负载低，在多种环境条件下仍具鲁棒性，为大规模光伏巡检提供了可扩展且可靠的解决方案。本研究展示了先进AI技术与实际工程应用的融合，推动了可再生能源系统中自动化故障检测的发展。
原文摘要 · Abstract (English)
Thermal anomaly detection in solar photovoltaic (PV) systems is essential for ensuring operational efficiency and reducing maintenance costs. In this study, we developed and named HOTSPOT-YOLO, a lightweight artificial intelligence (AI) model that integrates an efficient convolutional neural network backbone and attention mechanisms to improve object detection. This model is specifically designed for drone-based thermal inspections of PV systems, addressing the unique challenges of detecting small and subtle thermal anomalies, such as hotspots and defective modules, while maintaining real-time performance. Experimental results demonstrate a mean average precision of 90.8%, reflecting a significant improvement over baseline object detection models. With a reduced computational load and robustness under diverse environmental conditions, HOTSPOT-YOLO offers a scalable and reliable solution for large-scale PV inspections. This work highlights the integration of advanced AI techniques with practical engineering applications, revolutionizing automated fault detection in renewable energy systems.
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