boc/atm-anomaly-detection/README.md

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

• Project structure
• Database schema
• Data pipeline
• Model training
• API deployment