Naeem Umair, Chadda Ken, Vahaji Sara, Ahmad Jawad, Li Xiaodong, Asadi Ehsan
Department of Mechanical, Manufacturing and Mechatronics Engineering, RMIT University, Melbourne, VIC 3083, Australia.
Yellowfin Robotic Solutions Melbourne, Melbourne, VIC 3000, Australia.
Sensors (Basel). 2025 Jan 25;25(3):738. doi: 10.3390/s25030738.
Unmanned Aerial Vehicles (UAVs) integrated with lightweight visual cameras hold significant promise in renewable energy asset inspection and monitoring. This study presents an AI-assisted soiling detection methodology for inspecting solar photovoltaic (PV) panels, using UAV-captured RGB images. The proposed scheme introduces an autonomous end-to-end soiling detection model for common types of soiling in solar panel installations, including bird droppings and dust. Detecting soiling, particularly bird droppings, is critical due to their pronounced negative impact on power generation, primarily through hotspot formation and their resistance to natural cleaning processes such as rain. A dataset containing aerial RGB images of PV panels with dust and bird droppings is collected as a prerequisite. This study addresses the unique challenges posed by the small size and indistinct features of bird droppings in aerial imagery in contrast to relatively large-sized dust regions. To overcome these challenges, we developed a custom model, named SDS-YOLO (Soiling Detection System YOLO), which features a Convolutional Block Attention Module (CBAM) and two dedicated detection heads optimized for dust and bird droppings. The SDS-YOLO model significantly improves detection accuracy for bird droppings while maintaining robust performance for the dust class, compared with YOLOv5, YOLOv8, and YOLOv11. With the integration of CBAM, we achieved a substantial 40.2% increase in mean Average Precision (mAP50) and a 26.6% improvement in F1 score for bird droppings. Dust detection metrics also benefited from this attention-based refinement. These results underscore the CBAM's role in improving feature extraction and reducing false positives, particularly for challenging soiling types. Additionally, the SDS-YOLO parameter count is reduced by 24%, thus enhancing its suitability for edge computing applications.
集成轻型视觉相机的无人机在可再生能源资产检查和监测方面具有巨大潜力。本研究提出了一种用于检查太阳能光伏（PV）板的人工智能辅助污染检测方法，该方法使用无人机拍摄的RGB图像。所提出的方案引入了一种自主的端到端污染检测模型，用于检测太阳能板安装中常见类型的污染，包括鸟粪和灰尘。检测污染，尤其是鸟粪，至关重要，因为它们对发电有明显的负面影响，主要是通过形成热点以及它们对雨水等自然清洁过程的抗性。作为前提条件，收集了一个包含带有灰尘和鸟粪的光伏板航空RGB图像的数据集。本研究解决了与相对较大尺寸的灰尘区域相比，航空图像中鸟粪尺寸小且特征不明显所带来的独特挑战。为了克服这些挑战，我们开发了一个名为SDS - YOLO（污染检测系统YOLO）的定制模型，该模型具有卷积块注意力模块（CBAM）以及针对灰尘和鸟粪优化的两个专用检测头。与YOLOv5、YOLOv8和YOLOv11相比，SDS - YOLO模型显著提高了鸟粪的检测精度，同时保持了对灰尘类别的稳健性能。通过集成CBAM，我们在平均精度均值（mAP50）上实现了40.2%的大幅提升，在鸟粪的F1分数上提高了26.6%。灰尘检测指标也受益于这种基于注意力的优化。这些结果强调了CBAM在改善特征提取和减少误报方面的作用，特别是对于具有挑战性的污染类型。此外，SDS - YOLO的参数数量减少了24%，从而增强了其对边缘计算应用的适用性。
