metadata
license: other
license_name: commercial
license_link: LICENSE
tags:
- object-detection
- yolo
- yolov8
- anomaly-detection
- industrial-inspection
- gas-infrastructure
pipeline_tag: object-detection
Anomaly Detection YOLOv8s — V11
Production-grade anomaly detection model for underground gas valve well inspection. Identifies 6 types of structural and environmental anomalies in inspection photos.
Model Performance
| Metric | Value |
|---|---|
| mAP50 | 65.72% |
| mAP50-95 | 50.91% |
| Model | YOLOv8s |
| Parameters | 11.1M |
| Input Size | 640×640 |
| Training Images | 1,512 (VLM-annotated) |
| Fine-tuned from | V9 champion model |
Per-class Performance (on V2s val set, 201 images)
| Class | mAP50 | mAP50-95 |
|---|---|---|
| Water Accumulation (积水) | 84.1% | 65.1% |
| Water Seepage (渗水) | 65.2% | 47.6% |
| Corrosion / Rust (腐蚀生锈) | 61.9% | 45.9% |
| Coating Damage (涂层损坏) | 39.7% | 24.2% |
| Wall Crack (墙体裂缝) | 69.3% | 55.3% |
| Fog / Condensation (雾气结露) | 74.1% | 67.4% |
Training History
| Version | mAP50 | Training Images | Key Change |
|---|---|---|---|
| V2s | 48.2% | 512 | Baseline, VLM-annotated |
| V6 | 55.71% | 1,062 | VLM-only breakthrough, fine-tuned from V2s |
| V7 | 56.0% | 2,779 | Pseudo-labels added — no improvement |
| V8 | 60.51% | 1,464 | Fine-tuned from V6, more VLM data |
| V9 | 63.72% | 1,463 | Fine-tuned from V8, more VLM data |
| V11 | 65.72% | 1,512 | Fine-tuned from V9, consistent val split |
Key Insights
- Iterative fine-tuning works: Each generation fine-tuned from the previous best shows consistent gains (+8.6%, +5.3%, +3.1%)
- Pseudo-labels don't help: V7 (2,779 images with pseudo-labels) performed identically to V6 (1,062 VLM-only)
- Val split matters: V10 used a different val split and regressed; V11 with V9's split confirmed +3.1% gain
- Weak classes need focused data: water_seepage gained +10.4% with targeted VLM annotations; coating_damage still lags at 39.7%
Quick Start
pip install ultralytics
from ultralytics import YOLO
model = YOLO("best_v11.pt")
results = model.predict(source="inspection_photo.jpg", conf=0.3)
for r in results:
for box in r.boxes:
cls = int(box.cls[0])
conf = float(box.conf[0])
print(f"Anomaly: {model.names[cls]}, Confidence: {conf:.2f}")
Model Files
| File | Format | Size |
|---|---|---|
best_v11.pt |
PyTorch | 21.5 MB |
best_v11.onnx |
ONNX | 42.7 MB |
best_v11.torchscript |
TorchScript | 42.8 MB |
best_v11.mlpackage/ |
CoreML | 42.7 MB |
Anomaly Classes & Severity
| Class | Severity | Description |
|---|---|---|
| Water Accumulation | Medium | Standing water at well bottom |
| Water Seepage | High | Water seeping through walls/joints |
| Corrosion / Rust | High | Metal rust, pipe corrosion |
| Coating Damage | Medium | Peeling paint, coating deterioration |
| Wall Crack | Critical | Structural cracks in walls |
| Fog / Condensation | Low | Condensation, moisture in air |
Dataset Statistics (861,367 images analyzed)
- 94.5% of images have at least one anomaly
- Corrosion/Rust: 77.3% prevalence
- Coating Damage: 67.7% prevalence
- Wall Crack: 59.6% prevalence
- Water Seepage: 46.1% prevalence
- Water Accumulation: 44.4% prevalence
- Fog Condensation: 11.0% prevalence
Companion Model
For valve detection (gate, globe, ball, other), see our Valve Detection Model (mAP50=92.95%).
Resources
License
- Non-commercial use: Free for research, education, and personal projects
- Commercial use: Requires a paid license. See Product Page for pricing.