Multi-modal PV defect dataset

We present a small novel multi-modal image dataset collected from a commercial Floating Photovoltaic (FPV) installation, combining thermal imaging with color information through an innovative channel-wise fusion approach.The dataset consists of 10 synchronized image pairs captured at 2.5m altitude under clear weather conditions, where the Red channel contains calibrated thermal data, while the Green and Blue channels incorporate the luminance (Y) and chrominance (U) components, respectively, derived from YUV-transformed RGB images.The dataset encompasses seven distinct classes of PV module conditions: Hotspots, Bird Droppings, Dirt Accumulation, Heated Junction Boxes, intact PV Modules, Residual Damage, and compound Hotspots caused by Bird Droppings or Dirt. This unique combination enables simultaneous analysis of thermal signatures and visual features within a single image structure, facilitating research in automated inspection and fault detection in floating solar installations.Each image is accompanied by detailed metadata including capture conditions, calibration parameters, and ground truth annotations for all seven classes. The dataset's structure allows for straightforward integration with existing computer vision frameworks while providing new opportunities for developing algorithms specifically tailored to FPV installation monitoring and maintenance. This resource addresses the growing need for specialized multi-modal datasets in the renewable energy sector and supports research in thermal-visual fusion applications for solar installation inspection.