| --- |
| 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. |
|
|
