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76 lines
2.4 KiB
Markdown
76 lines
2.4 KiB
Markdown
# ATM Anomaly Detection
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AI-driven anomaly detection for ATM infrastructure monitoring.
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## Overview
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This system detects anomalies in ATM images using computer vision and machine learning:
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- **Physical damage** (vandalism, scratches, broken screens)
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- **Environmental issues** (graffiti, dirt, obstructions)
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- **Functional problems** (out of service, paper jams, empty cash)
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- **Security concerns** (skimming devices, suspicious attachments)
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## Structure
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```
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atm-anomaly-detection/
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├── data/ # Training data and datasets
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│ ├── raw/ # Original ATM images
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│ ├── processed/ # Preprocessed images
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│ ├── annotations/ # Label files
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│ └── splits/ # Train/val/test splits
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├── models/ # Trained model artifacts
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│ ├── checkpoints/ # Training checkpoints
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│ ├── exports/ # ONNX/TensorRT exports
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│ └── configs/ # Model configurations
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├── src/ # Source code
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│ ├── data/ # Data loading and preprocessing
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│ ├── models/ # Model architectures
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│ ├── training/ # Training loops
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│ ├── inference/ # Prediction pipeline
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│ └── evaluation/ # Metrics and validation
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├── config/ # Configuration files
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├── docs/ # Documentation
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└── scripts/ # Utility scripts
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```
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## Quick Start
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1. Place ATM images in `data/raw/`
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2. Run preprocessing: `python src/data/preprocess.py`
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3. Train model: `python src/training/train.py`
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4. Run inference: `python src/inference/predict.py --image <path>`
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## Data Schema
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### Images
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- Format: JPG/PNG
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- Resolution: 1920x1080 or higher
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- Naming: `{atm_id}_{timestamp}_{camera_angle}.jpg`
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### Annotations
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- Format: COCO JSON or YOLO txt
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- Categories: damage, graffiti, obstruction, skimming, out_of_service
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## Model
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- Base: YOLOv8 or EfficientDet
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- Input: 640x640 RGB
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- Output: Bounding boxes + anomaly class + confidence
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## Pipeline
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1. **Data Collection** → ATM images from field cameras
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2. **Preprocessing** → Resize, normalize, augment
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3. **Training** → Supervised learning on annotated data
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4. **Inference** → Real-time anomaly detection
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5. **Alerting** → Notify when anomalies detected
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## Status
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- [x] Project structure
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- [ ] Database schema
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- [ ] Data pipeline
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- [ ] Model training
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- [ ] API deployment
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