Solar Panel Inspection

About this solution

The AI cuts out each photovoltaic panel from the image (drone, fixed camera or inspection photo) and checks for the non-conformities that hurt a plant's generation and safety the most: soiling (dust, bird droppings, leaves, snow), cracked glass or broken modules, and loose or exposed wiring. Each panel gets a status (Compliant, Soiling X%, Broken, Exposed wiring), generation loss is estimated and panels are ranked by maintenance priority — the O&M team goes straight to the modules that most need cleaning or replacement.

1. 1Panel segmentation (YOLO11-seg): the outline of each module/table is cut out; in drone video each panel is tracked and counted only once.
2. 2Defect segmentation (YOLO11-seg): dust, bird droppings, leaves and physical damage are cut out in the actual shape of the stain. Only what is INSIDE the panel counts — surrounding ground and roof are ignored.
3. 3A second detector adds snow and confirms damage; CLIP (vision-language, no training) compares each panel with descriptions of clean, dirty, snow-covered and broken panels — damage is only confirmed with two votes.
4. 4Soiling = % of the panel area covered by stains (dust, droppings, leaves, snow). For snow, coverage is measured by the panel's white pixels.
5. 5Loose/exposed wiring: text-prompted detection (OWLv2) of hanging cables, loops and loose coils near the modules or on the structure.
6. 6Estimated generation loss: proportional to the soiled area (see assumptions); a broken panel assumes the configured loss (default 33%, one of the three bypass diodes of a typical module).
7. 7Maintenance ranking: broken panels and exposed wiring first (safety), then the panels with the highest estimated loss.