Utility-scale coverage
Process imagery from large solar sites without inspection teams reviewing every captured frame by hand.
Solar Panel Inspection
Detect, classify and prioritize solar panel defects from thermal and RGB imagery using AI software compatible with your existing drone inspection workflow.
- ReportsFaster turnaround
- AnalysisFaster than manual
- Less manualImage review
Inspection findings · Samplelive
- Hotspot · Row 12Analysing…
- Diode fault · Row 14Queued
- Soiling · Row 09Queued
- Cracked glass · Row 03Queued
Report ready for your maintenance system
Results
The outcomes we design for, and measure with you, on every solar inspection program.
Panel-level analysis
Findings are tied to individual panels, rows and strings so technicians know exactly where to go.
Multiple defect categories
Detect heat patterns, diode issues, degradation, cracks, soiling and physical damage in one workflow.
Faster report turnaround
Move from drone data to verified, maintenance-ready findings in a fraction of manual review time.
Results vary with imagery quality, sensor type, site conditions, defect scope and review requirements. Figures are never guaranteed.
Capabilities
Transform thermal and RGB drone data into verified, panel-level findings that support faster maintenance decisions across commercial and utility-scale solar installations.
Thermal and RGB Defect Detection
Analyze radiometric thermal and high-resolution RGB imagery to identify abnormal heat patterns, visible damage, contamination and panel-level performance anomalies.
Panel-Level Defect Localization
Associate each detected defect with its panel, row, string, GPS location, thermal evidence and corresponding visual imagery.
Soiling and Shading Analysis
Detect dust, debris, bird droppings, vegetation and recurring shadows that can obstruct panels and reduce expected energy generation.
Severity Scoring and Prioritization
Classify anomalies by defect type, temperature variation, model confidence and maintenance urgency to separate immediate repairs from monitored conditions.
Repeat Inspection Comparison
Compare current and historical inspection results to track defect progression, confirm completed repairs and identify recurring module-level problems.
Automated Inspection Reporting
Generate annotated findings, defect summaries, maintenance priorities, inspection records and system-ready exports without manually compiling every report.
Benefits
Replace slow panel walks and fragmented image review with automated drone analysis that helps maintenance teams identify performance-threatening defects earlier.
Inspect more panels
Process imagery from thousands of solar panels without requiring inspection teams to manually examine every module or captured frame.
Detect defects earlier
Identify thermal and visible abnormalities before undetected panel problems cause prolonged energy losses, equipment damage or expensive reactive maintenance.
Reduce field risk
Use drones for solar panel inspection to minimize unnecessary roof access, panel walks, electrical exposure and hazardous fieldwork.
Prioritize maintenance
Rank detected defects by category, confidence, severity and operational priority so technicians know which panels require attention first.
Detection
Train solar inspection models to recognize site-specific thermal and visual patterns associated with electrical faults, contamination, degradation and physical damage.
Hotspots
Identify abnormal cell or module heating associated with mismatch, electrical resistance, localized damage, shading or developing component failures.
Diode Failures
Detect distinctive sub-string heat patterns that may indicate failed, short-circuited, disconnected or improperly functioning bypass diodes.
Potential-Induced Degradation
Locate module-level and string-level thermal patterns that may indicate voltage-related degradation and declining photovoltaic system performance.
Crack-Related Anomalies
Detect visible or thermal patterns associated with cell damage and support electroluminescence imagery when detailed micro-crack confirmation is required.
Soiling and Debris
Identify dust buildup, bird droppings, leaves, vegetation and other surface obstructions that can affect panel output.
Physical and Installation Damage
Detect cracked glass, displaced modules, frame damage, misalignment, delamination indicators and other visible panel abnormalities.
How It Works
Move from drone data capture to verified maintenance actions through a structured inspection workflow designed around your sites, equipment and operational systems.
Drone data capture
Capture radiometric thermal and high-resolution RGB imagery using compatible commercial drones, camera payloads or custom solar inspection rigs.
Data upload and ingestion
Upload inspection imagery, sensor metadata, GPS coordinates, flight information and available site maps through automated or API-based workflows.
Panel detection and mapping
Identify individual solar panels and map each module to its precise location across the inspected site.
Defect detection and classification
Apply custom-trained computer vision models to detect and classify visible and thermal anomalies across captured inspection data.
Validation and severity scoring
Qualified reviewers verify AI findings before confirmed defects are scored by severity, confidence and maintenance priority.
Reporting and system integration
Deliver approved findings to CMMS, SCADA, GIS, ERP, asset-management platforms or custom systems.
Compatibility
Use compatible commercial drones and imaging systems without locking your inspection program into a single aircraft manufacturer or hardware ecosystem.
Commercial Drone Compatibility
Process suitable imagery from supported commercial drone platforms used for photovoltaic inspection workflows.
Thermal Sensor Support
Analyze compatible radiometric thermal imagery containing the temperature data and metadata required for dependable solar anomaly assessment.
RGB Camera Support
Process high-resolution RGB imagery to detect visible damage, debris, soiling, vegetation, misalignment and surface-level abnormalities.
Custom Inspection Rigs
Integrate imagery from custom drone platforms and sensor configurations when their data formats, metadata and image quality meet project requirements.
Flexible Data Ingestion
Upload inspection data directly or connect third-party flight platforms through APIs, cloud storage and configurable ingestion pipelines.
No Vendor Lock-In
Retain suitable drones and sensors while using Aarvexa AI as the analysis, reporting and integration layer.
Data Quality
Configure data capture and analysis around project requirements, environmental conditions, sensor limitations and applicable photovoltaic thermography guidance.
Sensor requirements
Define thermal sensitivity, radiometric capability, RGB resolution, metadata availability and other sensor requirements before conducting production inspections.
Flight configuration
Set appropriate altitude, speed, camera angle, image overlap and ground sampling distance for the targeted defect categories.
Environmental conditions
Account for irradiance, wind, cloud cover, temperature, reflections and other environmental factors affecting thermal inspection reliability.
Site mapping
Connect panel, row, string, inverter and geographic data so every confirmed defect can be accurately located.
Inspection validation
Establish model thresholds, reviewer requirements, defect definitions and acceptance criteria before measuring inspection performance.
Standards-aware workflows
Support inspection processes configured around applicable photovoltaic thermography guidance, such as IEC TS 62446-3, when required by the project.
We support your inspection program with workflows and documentation. We do not certify conformance to any standard on your behalf.
Deliverables
Give asset managers and technicians structured evidence they can use to locate defects, prioritize work and document corrective actions.
Georeferenced orthomosaics
View thermal and RGB site maps containing spatially aligned panel imagery, annotations and detected defect locations.
Panel-level findings
Receive the affected panel, row, string, GPS coordinates, defect category, supporting imagery and AI confidence score.
Severity-ranked defects
Organize confirmed findings by severity and maintenance priority so field teams can address the most consequential problems first.
Thermal evidence
Include radiometric measurements, temperature differences and thermal imagery when supported by the supplied sensor data and inspection conditions.
Historical comparisons
Compare inspection cycles to monitor developing faults, verify repairs and understand how panel conditions change over time.
Flexible reports and exports
Deliver findings through PDF reports, CSV files, GIS layers, APIs, dashboards and maintenance-ready data formats.
Integration
Connect verified drone inspection results with the platforms your operations, engineering and maintenance teams already use.
CMMS platforms
Create maintenance tickets containing defect type, panel location, severity, evidence and recommended follow-up actions in compatible CMMS platforms.
SAP
Transfer structured inspection findings into supported SAP asset-management, maintenance-planning and work-order processes.
IBM Maximo
Send verified defects and supporting evidence to IBM Maximo for maintenance scheduling, assignment, tracking and resolution.
SCADA systems
Connect inspection results with available operational data to investigate alarms, performance losses and suspected equipment failures.
GIS and digital twins
Visualize panel-level findings on site maps and digital models for easier asset monitoring and maintenance coordination.
Custom systems
Use configurable APIs and exports to integrate inspection outputs with proprietary reporting, analytics and asset-management platforms.
Product names are listed as examples of systems we integrate with. Third-party trademarks belong to their owners.
Beyond Solar
Extend the same AI-powered inspection approach beyond solar installations to other renewable energy and critical infrastructure assets.
Solar Farms
Detect thermal anomalies, panel damage, soiling, degradation patterns and installation issues across utility-scale photovoltaic sites.
Commercial Rooftop Arrays
Inspect difficult-to-access rooftop systems while reducing manual roof walks, operational disruption and unnecessary worker exposure.
Wind Turbines
Analyze blade imagery for cracks, erosion, lightning damage, surface deterioration and other visible structural abnormalities.
Power Lines
Detect corrosion, damaged components, vegetation encroachment, missing hardware and visible abnormalities across electrical transmission infrastructure.
Thermal anomaly detection
Solar inspection impactIllustrative
Detection qualityTuned
Overlapping imagesAuto-excluded
Thermal anomaly reviewAutomated
Solar inspection case study
Automated solar panel inspection with AI-based thermal anomaly detection
A visual inspection technology provider needs a faster, more reliable way to analyze thermal imagery captured during solar farm inspections. Aarvexa can design a custom computer vision model that detects panel hotspots and diode failures while filtering duplicate, overlapping and out-of-boundary images from inspection results.
Hotspotsabnormal heat patterns flagged on panels
Diode failuressub-string heat signatures detected
Automated reviewthermal image analysis streamlined
- Built a custom AI model for thermal solar panel inspection imagery.
- Detected hotspot and diode failure patterns across uploaded inspection images.
- Removed overlapping captures and imagery outside panel boundaries.
- Integrated automated anomaly detection into the client's existing inspection portal.
Illustrative scenario. Details are representative, not client-specific, and outcomes vary by imagery quality, sensor type, defect scope and site conditions.
Explore a similar buildWhy Aarvexa
Combine custom computer vision, hardware flexibility, human validation and direct system integration within one deployable solar inspection workflow. 1,000+ projects delivered and a 98% successful go-live rate.
Hardware-agnostic AI layer
Use compatible drones, thermal cameras, RGB sensors and inspection rigs without being tied to a single manufacturer or proprietary hardware ecosystem.
Custom-trained, benchmarked models
Train and evaluate models using your panel types, defect classes, site conditions, sensors and representative imagery before production deployment.
Precise defect localization
Locate thermal and visible anomalies at panel level with coordinates, supporting imagery and asset references for faster field verification.
Direct CMMS and ERP integration
Send approved defect findings into your existing maintenance, asset-management, GIS, SCADA or work-order systems.
16+ years of engineering expertise
Work with an experienced team combining software engineering, computer vision development, system integration and performance optimization.
Validated on your data
Accuracy is benchmarked on suitable production datasets, subject to imagery quality, defect scope and site conditions.
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FAQ
Straight answers on solar panel inspection, detection and accuracy, drones and data, integration, pricing and who we are.
It analyzes thermal and RGB imagery captured by drones to detect, classify and prioritize solar panel defects, then delivers panel-level findings and reports your maintenance teams can act on.
Hotspots, diode failures, potential-induced degradation patterns, crack-related anomalies, soiling and debris, and physical or installation damage. Models are trained around the patterns on your sites.
Often not. We work with suitable commercial drones, radiometric thermal sensors, RGB cameras and custom rigs, provided data formats, metadata and image quality meet project requirements.
Solar asset owners, operators, O&M providers, EPCs and inspection service companies running utility-scale, commercial and rooftop solar programs.
A short consultation and a pilot on a sample of your own imagery is the usual start, to validate defect classes, accuracy and reporting before scaling to full sites.