标题： HOTSPOT-YOLO：一种轻量级深度学习注意力驱动模型，用于检测基于无人机的太阳能光伏检查中的热异常 显示英文标题

标题： HOTSPOT-YOLO: A Lightweight Deep Learning Attention-Driven Model for Detecting Thermal Anomalies in Drone-Based Solar Photovoltaic Inspections

摘要： 热能异常检测在太阳能光伏(PV)系统中对于确保运行效率和降低维护成本至关重要。 在本研究中，我们开发并命名了HOTSPOT-YOLO，这是一种轻量级人工智能(AI)模型，它集成了高效的卷积神经网络主干和注意力机制以提高目标检测。 该模型专门用于基于无人机的PV系统热检测，解决了检测小型和细微热异常（如热点和有缺陷的模块）的独特挑战，同时保持实时性能。 实验结果表明平均精度为90.8%，反映了相对于基线目标检测模型的显著改进。 在计算负载减少和在各种环境条件下具有鲁棒性的情况下， HOTSPOT-YOLO为大规模PV检测提供了一个可扩展且可靠解决方案。 这项工作突出了先进AI技术与实际工程应用的结合，革新了可再生能源系统中的自动化故障检测。 显示英文摘要

摘要： 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.