Autonomous Inspection Agents

Inspection Agent Architecture

┌─────────────────────────────────────────────────┐
│              Inspection Agent                    │
├─────────────────────────────────────────────────┤
│  Input Layer                                     │
│  - Video ingestion (drone, camera, upload)       │
│  - Enhancement (BetterVideo API)                 │
├─────────────────────────────────────────────────┤
│  Detection Layer                                 │
│  - Defect detection models                       │
│  - Anomaly detection                             │
│  - Object/component recognition                  │
├─────────────────────────────────────────────────┤
│  Analysis Layer                                  │
│  - Severity classification                       │
│  - Root cause analysis                           │
│  - Trend detection                               │
├─────────────────────────────────────────────────┤
│  Reporting Layer                                 │
│  - Structured reports                            │
│  - Visual annotations                            │
│  - Alerts and notifications                      │
└─────────────────────────────────────────────────┘

Use Cases

Infrastructure Inspection

• Bridge and road condition assessment
• Power line and utility inspection
• Building facade inspection
• Pipeline monitoring

Manufacturing Quality Control

• Product defect detection
• Assembly verification
• Surface finish inspection
• Dimensional verification

Property & Real Estate

• Roof damage assessment
• Property condition reports
• Construction progress monitoring
• Insurance claim documentation

Drone Integration

Drones capture video that agents analyze:

class DroneInspectionPipeline:
    async def inspect(self, drone_video_url: str) -> InspectionReport:
        # 1. Enhance drone footage (often compressed, shaky)
        enhanced = await bettervideo.enhance(drone_video_url)

        # 2. Extract frames at regular intervals
        frames = extract_frames(enhanced, interval=2.0)  # Every 2 seconds

        # 3. Run defect detection on each frame
        detections = []
        for frame in frames:
            defects = self.defect_model.detect(frame)
            if defects:
                detections.append({
                    "timestamp": frame.timestamp,
                    "gps": frame.gps_coordinates,
                    "defects": defects
                })

        # 4. Generate report with LLM
        report = await self.generate_report(detections)

        return report

Defect Detection Models

Options for defect detection:

Pre-trained Models

• General object detection (YOLO): Detect known defect types
• Anomaly detection: Find anything unusual without specific training

Custom-Trained Models

• Train on your specific defect types
• Higher accuracy for known defects
• Requires labeled training data

Vision-Language Models

• Describe defects in natural language
• No training required
• Good for novel defect types

# Using VLM for inspection
response = vision_model.analyze(
    image=frame,
    prompt="""
    You are inspecting a roof for damage.
    Identify any: missing shingles, cracks, water damage, debris, or structural issues.
    For each defect, describe its location and severity.
    """
)

Generating Reports

REPORT_TEMPLATE = """
# Inspection Report

## Summary
{summary}

## Defects Found

{defects_section}

## Recommendations
{recommendations}

## Appendix: All Frames Analyzed
{frame_list}
"""

async def generate_report(self, detections: List) -> str:
    # Use LLM to write human-readable report
    prompt = f"""
    Generate an inspection report based on these detections:
    {detections}

    Include:
    1. Executive summary
    2. Detailed defect descriptions with severity
    3. Recommended actions
    4. Priority ranking
    """

    report_content = await llm.complete(prompt)

    # Add visual annotations
    annotated_frames = self.annotate_defects(detections)

    return format_report(report_content, annotated_frames)