Solar Panel Defect Detection with Drones and AI: Complete Guide

Tutorial Industry
solar-panels drone-inspection thermal-imaging renewable-energy infrastructure

Hardware Used

Drones Thermal cameras RGB cameras GPS systems

Software Stack

Python OpenCV Deep learning Mapping software

Use Cases

Solar farm inspection Renewable energy Preventive maintenance Asset management
James Lions James Lions February 23, 2026 15 min read

Introduction

Solar farms require regular inspection to maintain efficiency. A single faulty panel can affect entire strings, and undetected issues compound over time. Manual inspection is slow, expensive, and misses subtle defects.

Drone-based inspection with AI analysis is 80% faster than manual methods and catches defects humans miss. Thermal imaging reveals issues invisible to the naked eye, while AI processes thousands of images to pinpoint problems automatically.

Common Solar Panel Defects

Detectable with Thermal Imaging

Defect Thermal Signature Cause Impact
Hotspot Localized high temp Cell damage, shading, connection 10-25% power loss
Substring failure 1/3 of panel hot Bypass diode activated 33% panel loss
Full panel failure Entire panel hot/cold Connection issue 100% panel loss
PID (Potential Induced Degradation) Gradient pattern Voltage leakage Progressive loss
Delamination Irregular hot zones Moisture ingress Long-term failure

Detectable with Visual/RGB Imaging

Defect Visual Appearance Cause Detection Method
Cracks Lines across cells Impact, thermal stress Edge detection
Snail trails Silvery/brown lines Moisture + silver migration Pattern recognition
Soiling Dirt, bird droppings Environmental Color/texture analysis
Vegetation Shadows on panels Overgrown plants Shadow detection
Physical damage Broken glass, frames Weather, impacts Object detection

System Architecture

Complete Inspection Pipeline

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┌─────────────────┐     ┌──────────────────┐     ┌────────────────┐
│  Flight Planning │────►│  Drone Flight    │────►│ Image Capture  │
│  (GIS mapping)   │     │  (Automated)     │     │ (Thermal+RGB)  │
└─────────────────┘     └──────────────────┘     └───────┬────────┘
                                                         │
┌─────────────────┐     ┌──────────────────┐     ┌───────▼────────┐
│  Report Gen     │◄────│  AI Analysis     │◄────│ Image Stitching│
│  (Locations)    │     │  (Defect Det.)   │     │ (Orthomosaic)  │
└─────────────────┘     └──────────────────┘     └────────────────┘

Components

1. Drone Platform

  • Multirotor (DJI, Autel, custom)
  • RTK GPS for accurate positioning
  • Automated flight capability
  • 20-40 minute flight time

2. Camera Payload

  • Thermal camera (radiometric)
  • RGB camera (for visual reference)
  • Gimbal stabilization
  • Synchronized capture

3. Ground Station

  • Flight planning software
  • Live telemetry
  • Image download
  • Basic QC checks

4. Processing Pipeline

  • Image stitching/orthomosaic
  • AI defect detection
  • GIS integration
  • Report generation

Thermal Imaging Fundamentals

How It Works

Solar panels convert sunlight to electricity. Defective cells convert more energy to heat instead. Thermal cameras detect this temperature difference.

Key Concepts:

Emissivity: Solar panels have high emissivity (~0.85-0.95), making them good thermal targets.

Temperature Delta (ΔT): Difference between defective area and healthy reference.

  • ΔT > 10°C: Significant issue
  • ΔT > 20°C: Critical defect
  • ΔT > 40°C: Immediate attention

Imaging Conditions:

  • Minimum 500 W/m² irradiance
  • Stable conditions (low wind)
  • Clear sky preferred
  • Midday for consistent results

Camera Requirements

Spec Minimum Recommended
Resolution 320×240 640×512+
Thermal Sensitivity <50mK <30mK
Accuracy ±2°C ±1°C
Radiometric Yes Yes
Frame Rate 9Hz 30Hz+

Popular options:

  • DJI Zenmuse H20T / H30T
  • FLIR Vue Pro R
  • Workswell Wiris Pro

Defect Detection Algorithms

Approach 1: Temperature Thresholding

Simple but effective for obvious defects.

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import cv2
import numpy as np

def detect_hotspots_threshold(thermal_image, delta_threshold=10):
    """
    Detect hotspots using temperature differential.

    thermal_image: Temperature values in Celsius (numpy array)
    delta_threshold: Temperature difference to flag (°C)
    """
    # Calculate reference temperature (median of panel)
    reference_temp = np.median(thermal_image)

    # Find areas significantly hotter
    hotspot_mask = thermal_image > (reference_temp + delta_threshold)

    # Clean up noise
    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
    hotspot_mask = hotspot_mask.astype(np.uint8) * 255
    hotspot_mask = cv2.morphologyEx(hotspot_mask, cv2.MORPH_OPEN, kernel)
    hotspot_mask = cv2.morphologyEx(hotspot_mask, cv2.MORPH_CLOSE, kernel)

    # Find hotspot regions
    contours, _ = cv2.findContours(
        hotspot_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
    )

    hotspots = []
    for contour in contours:
        area = cv2.contourArea(contour)
        if area > 100:  # Minimum size
            x, y, w, h = cv2.boundingRect(contour)

            # Get max temperature in region
            roi = thermal_image[y:y+h, x:x+w]
            max_temp = np.max(roi)

            hotspots.append({
                'bbox': (x, y, w, h),
                'area': area,
                'max_temp': max_temp,
                'delta_t': max_temp - reference_temp,
                'severity': classify_severity(max_temp - reference_temp)
            })

    return hotspots

def classify_severity(delta_t):
    if delta_t > 40:
        return 'critical'
    elif delta_t > 20:
        return 'high'
    elif delta_t > 10:
        return 'medium'
    else:
        return 'low'

Approach 2: Panel Segmentation + Analysis

Segment individual panels, then analyze each.

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def segment_panels(thermal_image, rgb_image=None):
    """
    Segment individual solar panels from image.
    Uses edge detection and line finding.
    """
    # Normalize thermal for edge detection
    normalized = cv2.normalize(
        thermal_image, None, 0, 255, cv2.NORM_MINMAX
    ).astype(np.uint8)

    # Edge detection
    edges = cv2.Canny(normalized, 50, 150)

    # Find lines (panel edges)
    lines = cv2.HoughLinesP(
        edges, 1, np.pi/180, 50,
        minLineLength=50, maxLineGap=10
    )

    # Group lines into panels (simplified)
    # In production, use more sophisticated panel detection

    # Find contours as panel candidates
    dilated = cv2.dilate(edges, np.ones((3,3), np.uint8))
    contours, _ = cv2.findContours(
        dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
    )

    panels = []
    for contour in contours:
        area = cv2.contourArea(contour)
        if area > 1000:  # Minimum panel size
            rect = cv2.minAreaRect(contour)
            box = cv2.boxPoints(rect)
            panels.append({
                'contour': contour,
                'rect': rect,
                'box': np.int0(box),
                'area': area
            })

    return panels

def analyze_panel(thermal_image, panel_mask):
    """
    Analyze single panel for defects.
    """
    # Extract panel temperatures
    panel_temps = thermal_image[panel_mask > 0]

    if len(panel_temps) == 0:
        return None

    analysis = {
        'mean_temp': np.mean(panel_temps),
        'max_temp': np.max(panel_temps),
        'min_temp': np.min(panel_temps),
        'std_temp': np.std(panel_temps),
        'temp_range': np.max(panel_temps) - np.min(panel_temps)
    }

    # Classify panel health
    if analysis['temp_range'] > 15:
        analysis['status'] = 'defective'
        analysis['issue'] = 'high_variance'
    elif analysis['max_temp'] > analysis['mean_temp'] + 20:
        analysis['status'] = 'defective'
        analysis['issue'] = 'hotspot'
    else:
        analysis['status'] = 'healthy'
        analysis['issue'] = None

    return analysis

Approach 3: Deep Learning Detection

Train YOLO or similar to detect defect patterns.

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from ultralytics import YOLO

# Training config for solar panel defects
"""
# solar_defects.yaml
path: /data/solar_panels
train: images/train
val: images/val

names:
  0: hotspot
  1: substring_failure
  2: full_failure
  3: soiling
  4: crack
  5: snail_trail
  6: vegetation_shadow
"""

# Train model
model = YOLO('yolov8m.pt')
results = model.train(
    data='solar_defects.yaml',
    epochs=150,
    imgsz=640,
    batch=16,
    augment=True
)

# Inference
def detect_defects_yolo(thermal_path, model_path):
    model = YOLO(model_path)
    results = model(thermal_path)

    defects = []
    for result in results:
        for box in result.boxes:
            defects.append({
                'class': result.names[int(box.cls)],
                'confidence': float(box.conf),
                'bbox': box.xyxy[0].tolist()
            })

    return defects

Drone Operations

Flight Planning

Coverage Parameters:

  • Altitude: 15-30m AGL (higher = faster, lower = more detail)
  • Overlap: 70% front, 60% side (for stitching)
  • Speed: 3-5 m/s typical
  • GSD: 2-5 cm/pixel RGB, 5-15 cm thermal

Flight Pattern:

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────────────────────►
◄────────────────────
────────────────────►
◄────────────────────

Simple lawnmower pattern over array.

Optimal Conditions

Factor Ideal Acceptable
Irradiance >700 W/m² >500 W/m²
Wind <5 m/s <10 m/s
Cloud Clear Stable overcast
Time 10am-2pm 9am-4pm
Panel load Operating Operating

Regulatory Considerations

  • Pilot certification (varies by country)
  • Site permissions
  • Airspace clearance
  • Insurance requirements
  • Visual observer requirements

Data Processing Pipeline

Image Stitching

Create orthomosaic from individual captures:

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# Using OpenDroneMap or similar
# Basic workflow:

# 1. Load images with GPS data
# 2. Feature matching between overlapping images
# 3. Bundle adjustment
# 4. Dense reconstruction
# 5. Orthophoto generation
# 6. GeoTIFF export with coordinates

# For thermal specifically:
# - Process thermal and RGB separately
# - Align using common features
# - Generate dual-layer orthomosaic

Georeferencing Defects

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import rasterio
from pyproj import Transformer

def pixel_to_coordinates(pixel_x, pixel_y, geotiff_path):
    """
    Convert pixel coordinates to lat/lon.
    """
    with rasterio.open(geotiff_path) as src:
        # Get transform
        transform = src.transform
        crs = src.crs

        # Pixel to projected coordinates
        x, y = rasterio.transform.xy(transform, pixel_y, pixel_x)

        # Project to WGS84 if needed
        if crs != 'EPSG:4326':
            transformer = Transformer.from_crs(crs, 'EPSG:4326', always_xy=True)
            lon, lat = transformer.transform(x, y)
        else:
            lon, lat = x, y

        return lat, lon

def generate_defect_report(defects, geotiff_path):
    """
    Generate georeferenced defect report.
    """
    report = []
    for defect in defects:
        x, y, w, h = defect['bbox']
        center_x = x + w/2
        center_y = y + h/2

        lat, lon = pixel_to_coordinates(center_x, center_y, geotiff_path)

        report.append({
            **defect,
            'latitude': lat,
            'longitude': lon,
            'google_maps_link': f"https://maps.google.com/?q={lat},{lon}"
        })

    return report

Hardware Recommendations

Budget Setup (~£2,000-5,000)

Component Example Cost
Drone DJI Mini 3 Pro £750
Thermal Camera FLIR One Pro £400
Mount/Integration DIY or 3rd party £200
Software Open source stack Free
Laptop Processing capable £800
Total   ~£2,150

Note: Integrated solutions like DJI Mavic 3 Thermal (~£4,000) simplify workflow.

Professional Setup (~£10,000-20,000)

Component Example Cost
Drone DJI Matrice 300/350 £8,000
Payload Zenmuse H20T £5,000
RTK Module Centimeter accuracy £1,500
Software Commercial platform £2,000/yr
Training Pilot certification £500

Business Model Considerations

Service Pricing (UK Market)

Service Level Typical Rate
Basic inspection (<1 MW) £200-400
Standard inspection (1-5 MW) £300-600
Large site (>5 MW) £50-100 per MW
Detailed report add-on +£100-200
Annual contract 20-30% discount

ROI for Solar Farm Operators

Cost of Undetected Defects:

  • 1 hotspot = 10-25% panel loss
  • String failure = multiple panels affected
  • Early detection prevents cascade failures
  • Insurance may require regular inspection

Inspection ROI Example:

  • 1 MW solar farm (~3,000 panels)
  • Annual production: ~1,000 MWh
  • Revenue at £0.10/kWh: £100,000/year
  • 5% production loss from undetected defects: £5,000/year
  • Drone inspection cost: £500/year
  • ROI: 10x inspection cost

Getting Started

Phase 1: Learn the Basics

  1. Understand thermal imaging principles
  2. Practice drone flying (non-commercial)
  3. Learn image processing fundamentals
  4. Study solar panel failure modes

Phase 2: Prototype

  1. Acquire basic thermal camera
  2. Capture sample thermal images
  3. Develop detection algorithms
  4. Validate on known defects

Phase 3: Field Testing

  1. Partner with solar farm operator
  2. Conduct supervised inspections
  3. Compare results to manual inspection
  4. Refine detection models

Phase 4: Commercialize

  1. Obtain necessary certifications
  2. Invest in professional equipment
  3. Develop reporting workflows
  4. Build client relationships

Looking to get started with drone inspection? The combination of growing solar capacity and aging installations makes this a rapidly expanding market.

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