# ATM Anomaly Detection

AI-driven anomaly detection for ATM infrastructure monitoring.

## Overview

This system detects anomalies in ATM images using computer vision and machine learning:
- **Physical damage** (vandalism, scratches, broken screens)
- **Environmental issues** (graffiti, dirt, obstructions)
- **Functional problems** (out of service, paper jams, empty cash)
- **Security concerns** (skimming devices, suspicious attachments)

## Structure

```
atm-anomaly-detection/
├── data/               # Training data and datasets
│   ├── raw/           # Original ATM images
│   ├── processed/     # Preprocessed images
│   ├── annotations/   # Label files
│   └── splits/        # Train/val/test splits
├── models/            # Trained model artifacts
│   ├── checkpoints/   # Training checkpoints
│   ├── exports/       # ONNX/TensorRT exports
│   └── configs/       # Model configurations
├── src/               # Source code
│   ├── data/          # Data loading and preprocessing
│   ├── models/        # Model architectures
│   ├── training/      # Training loops
│   ├── inference/     # Prediction pipeline
│   └── evaluation/    # Metrics and validation
├── config/            # Configuration files
├── docs/              # Documentation
└── scripts/           # Utility scripts
```

## Quick Start

1. Place ATM images in `data/raw/`
2. Run preprocessing: `python src/data/preprocess.py`
3. Train model: `python src/training/train.py`
4. Run inference: `python src/inference/predict.py --image <path>`

## Data Schema

### Images
- Format: JPG/PNG
- Resolution: 1920x1080 or higher
- Naming: `{atm_id}_{timestamp}_{camera_angle}.jpg`

### Annotations
- Format: COCO JSON or YOLO txt
- Categories: damage, graffiti, obstruction, skimming, out_of_service

## Model

- Base: YOLOv8 or EfficientDet
- Input: 640x640 RGB
- Output: Bounding boxes + anomaly class + confidence

## Pipeline

1. **Data Collection** → ATM images from field cameras
2. **Preprocessing** → Resize, normalize, augment
3. **Training** → Supervised learning on annotated data
4. **Inference** → Real-time anomaly detection
5. **Alerting** → Notify when anomalies detected

## Status

- [x] Project structure
- [ ] Database schema
- [ ] Data pipeline
- [ ] Model training
- [ ] API deployment