boc/atm-anomaly-detection/docs/DATASET_GUIDE.md

ATM Anomaly Detection Dataset Guide

Overview

This guide describes how to prepare training data for the ATM anomaly detection model.

Directory Structure

data/
├── raw/                    # Original images from cameras
│   ├── atm_001_20260701_120000_front.jpg
│   ├── atm_001_20260701_120005_side.jpg
│   └── ...
├── processed/              # Resized and normalized images
│   └── ...
├── annotations/            # Label files
│   ├── atm_001_20260701_120000_front.txt
│   └── ...
└── splits/                 # Train/val/test splits
    ├── train/
    │   ├── images/
    │   └── labels/
    ├── val/
    │   ├── images/
    │   └── labels/
    └── test/
        ├── images/
        └── labels/

Image Naming Convention

Format: {atm_id}_{timestamp}_{camera_angle}.jpg

Examples:

• atm_001_20260701120000_front.jpg
• atm_001_20260701120000_side.jpg
• atm_002_20260701123000_wide.jpg

Annotation Format (YOLO)

Each .txt file contains one line per object:

<class_id> <x_center> <y_center> <width> <height>

All values are normalized to [0, 1] relative to image dimensions.

Example:

0 0.45 0.52 0.12 0.08
5 0.78 0.35 0.05 0.03

Class IDs

ID	Class Name	Description
0	physical_damage	Visible damage to structure
1	vandalism	Intentional damage
2	graffiti	Unauthorized markings
3	dirt_debris	Excessive dirt or debris
4	obstruction	Objects blocking view/access
5	skimming_device	Card skimmer attached
6	suspicious_attachment	Unknown device attached
7	out_of_service	Machine not functioning
8	screen_damage	Cracked or broken screen
9	cash_jam	Cash dispenser issue
10	receipt_jam	Printer issue
11	lighting_failure	Poor or no lighting
12	camera_blind	Security camera blocked
13	network_down	Connectivity issue

Annotation Guidelines

Bounding Boxes

• Tight fit around anomaly
• Include entire affected area
• Do not include unaffected surroundings

Multiple Anomalies

• Each anomaly gets its own bounding box
• Overlapping boxes are OK
• Same-class overlaps: merge if touching

Difficult Cases

• Partially visible anomalies: annotate visible portion
• Ambiguous cases: mark with low confidence
• False positives in training: do not annotate

Data Collection Best Practices

Camera Setup

• Resolution: minimum 1920x1080
• Angle: front-facing, eye-level
• Lighting: avoid extreme shadows
• Distance: capture full ATM in frame

Coverage

• Multiple angles per ATM
• Different times of day
• Various weather conditions
• Both normal and anomalous states

Minimum Dataset Size

• Training: 1000+ images per class
• Validation: 200+ images per class
• Test: 200+ images per class

Augmentation Strategy

Applied during training:

• Horizontal flip (50%)
• Brightness ±20%
• Rotation ±5 degrees
• Scale 50-150%

Not applied (preserve realism):

• Vertical flip
• Extreme rotation
• Color distortion

Quality Checks

Before training:

1. Verify all images load correctly
2. Check annotation format
3. Validate bounding boxes within image bounds
4. Ensure class distribution is reasonable
5. Remove duplicates

Tools

• LabelImg - GUI annotation tool
• CVAT - Online annotation platform
• Roboflow - Dataset management