Two-Stage Lightweight CNN with Weakly Supervised Defect Localization for Multimodal Photovoltaic Fault Detection

This repository contains three distinct photovoltaic (PV) datasets representing the full spectrum of solar fault detection: internal cell degradation (EL), thermal anomalies (IR), and surface-level obstructions (RGB). Each dataset includes both the Original (raw) images and the Refined (auto-annotated) versions generated by our Two-Stage Framework. 1. Electroluminescence (EL) PV Dataset Modality: 8-bit Grayscale (Internal Inspection). Purpose: Used to detect subsurface defects invisible to the naked eye, such as microcracks and electrical disconnects. Content: The dataset comprises 2,624 images extracted from 44 distinct solar modules. It includes both monocrystalline (uniform background) and polycrystalline (complex grain boundary) cells. Classes: Binary classification (Functional vs. Defective). Challenge: The primary challenge is distinguishing actual microcracks from the jagged grain boundaries of polycrystalline cells, which often mimic defect patterns. 2. Thermal Infrared (IR) PV Dataset Modality: Thermal Infrared (Heat Anomaly Inspection). Purpose: Used to identify heat-induced faults, such as hotspots and diode failures, captured via UAVs. Content: A large-scale dataset of 20,000 thermal images. Resolution: Extremely low native resolution ($40 \times 24$ pixels), presenting a significant challenge for feature extraction. Classes: 12 Categories, including No-Anomaly, Hotspot, Hotspot-Multi, Diode, Diode-Multi, Shadowing, Cell, Cell-Multi, Cracking, Soiling, Vegetation, and Offline-Module. Challenge: Extreme class imbalance (50% of data is "No-Anomaly") and spatial sparsity. 3. Aerial RGB PV Dataset Modality: Optical RGB (Surface Inspection). Purpose: Used to classify surface-level obstructions and physical degradation visible from low-altitude drone flights. Content: 875 high-resolution images acquired under varying outdoor lighting conditions. Classes: 6 Categories: Clean, Dusty, Bird Drop, Electrical Damage, Physical Damage, and Snow-Covered. Challenge: Distinguishing between transient environmental obstructions (e.g., bird drops, snow) and permanent physical damage (e.g., shattered glass) amidst complex ground clutter. Dataset Structure in Zip Files Original Datasets: Contain the raw images exactly as collected/standardized. Refined Datasets: Contain the output of Phase 1 (Auto-Annotation). These images have been processed by the WSOL module to generate cropped or spatially focused regions of interest (ROI), removing background noise (e.g., roof tiles in RGB or grain boundaries in EL) to facilitate high-precision fine-tuning in Phase 2.