Ahnuf/Military_Aircraft_Detection_Classification_Image_Dataset

Military Aircraft Detection & Classification Dataset

88 Classes with Advanced Background Suppression

Overview

This dataset is a professionally curated resource for training high-performance object detection and image classification models such as YOLOv11. It contains 88 distinct military aircraft classes and is explicitly designed for real-world deployment, where false positives from civilian aircraft, birds, and small drones are common.

To address this, the dataset incorporates a structured background suppression strategy, teaching models not only what to detect, but also what to ignore.

Dataset Summary

• —Total Images: 26,668
• —Military Aircraft Classes: 87
• —Image Resolution: 640 × 640 (uniform)
• —Annotation Format: YOLO (.txt) with normalized coordinates
• —Primary Use: Military aircraft detection and classification

Dataset Split & Background Statistics

The dataset maintains a stratified split of approximately 80% Train / 10% Validation / 10% Test across all 87 classes.

Advanced Background Suppression Strategy

To significantly reduce false detections, the dataset includes 3,127 background-only images with empty annotations. These images are intentionally selected to represent common real-world confounders in aerial imagery.

Background Categories

1. 1.Empty Skies, Clouds & Commercial Aircraft Negative samples containing:
2. 2.Clear or cloudy skies with no aircraft
3. 3.Commercial passenger and cargo aircraft This trains the model to distinguish civilian airliners from military platforms.

1. 1.Bird Backgrounds (≈1.5%) High-resolution bird imagery to prevent bird-as-aircraft false positives, particularly at long range or low resolution.

1. 1.Commercial Drone Backgrounds (≈1.5%) Civilian and hobbyist UAVs (quadcopters and small drones), enabling the model to differentiate between commercial drones and military-grade UAVs.

All background images use empty `.txt` label files (0 bytes) and contain no bounding boxes.

Annotation Format

Each image is paired with a corresponding .txt file in YOLO format.

Positive Sample Example

su57_01.txt 68 0.475000 0.496875 0.415625 0.859375

Field Description

• —68 → Class ID (Su-57)
• —0.475000 → X-center (47.5% of image width)
• —0.496875 → Y-center (49.69% of image height)
• —0.415625 → Bounding box width
• —0.859375 → Bounding box height

Background (Negative) Samples

Background label files are intentionally empty:

• —sky_bg_01.txt
• —commercial_aircraft_bg_01.txt
• —birds_v1_01.txt
• —drones_v1_01.txt

Final Class ID Table (87 Classes)

Intended Use Cases

• —Military aircraft detection and classification
• —Civilian vs military aircraft discrimination
• —UAV and drone differentiation
• —Long-range aerial surveillance research
• —False-positive suppression benchmarking for YOLO models