---
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

```bash
pip install ultralytics
```

```python
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](https://huggingface.co/lg227210/valve-detection-yolov8s) (mAP50=92.95%).

## Resources

- [Live Demo](https://huggingface.co/spaces/lg227210/valve-detection-demo)
- [Anomaly Dataset](https://huggingface.co/datasets/lg227210/gas-valve-anomaly-dataset)
- [Technical Blog](https://dev.to/jins_zhu_10096cef92aba38b/how-we-trained-a-92-95-map50-valve-detection-model-with-iterative-pseudo-labeling-4lhi)
- [Source Code](https://github.com/zhuj92405-design/valve-detection-yolov8s2)
- [Product Page](https://zhuj92405-design.github.io/valve-detection-yolov8s2/)

## License

- **Non-commercial use**: Free for research, education, and personal projects
- **Commercial use**: Requires a paid license. See [Product Page](https://zhuj92405-design.github.io/valve-detection-yolov8s2/) for pricing.