| --- |
| license: apache-2.0 |
| task_categories: |
| - object-detection |
| - image-classification |
| tags: |
| - military |
| - aircraft |
| - aerospace |
| - yolo |
| - defense |
| - commercial-aircraft |
| - birds |
| - drones |
| size_categories: |
| - 10K-100K |
| --- |
| |
| # 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*. |
|
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| --- |
|
|
| ## 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 |
|
|
| | Split | Total Images | Background Images | Background % | |
| |------|-------------|------------------|--------------| |
| | Train | 21,342 | 2,508 | 11.75% | |
| | Validation | 2,641 | 295 | 11.17% | |
| | Test | 2,645 | 284 | 10.74% | |
| | **Total** | **26,668** | **3,127** | **~11.7%** | |
|
|
| 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. **Empty Skies, Clouds & Commercial Aircraft** |
| Negative samples containing: |
| - Clear or cloudy skies with no aircraft |
| - **Commercial passenger and cargo aircraft** |
| This trains the model to distinguish civilian airliners from military platforms. |
|
|
| 2. **Bird Backgrounds (≈1.5%)** |
| High-resolution bird imagery to prevent *bird-as-aircraft* false positives, particularly at long range or low resolution. |
|
|
| 3. **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) |
|
|
| | ID | Class | ID | Class | ID | Class | ID | Class | |
| |----|-------|----|-------|----|-------|----|-------| |
| | 0 | A10 | 22 | CL415 | 44 | JF17 | 66 | Su34 | |
| | 1 | A400M | 23 | E2 | 45 | JH7 | 67 | Su47 | |
| | 2 | AG600 | 24 | E7 | 46 | KAAN | 68 | Su57 | |
| | 3 | AH64 | 25 | EF2000 | 47 | KC135 | 69 | TB001 | |
| | 4 | AKINCI | 26 | EMB314 | 48 | KF21 | 70 | TB2 | |
| | 5 | AV8B | 27 | F117 | 49 | KJ600 | 71 | Tejas | |
| | 6 | An124 | 28 | F14 | 50 | Ka27 | 72 | Tornado | |
| | 7 | An22 | 29 | F15 | 51 | Ka52 | 73 | Tu160 | |
| | 8 | An225 | 30 | F16 | 52 | MQ9 | 74 | Tu22M | |
| | 9 | An72 | 31 | F18 | 53 | Mi24 | 75 | Tu95 | |
| | 10 | B1 | 32 | F2 | 54 | Mi26 | 76 | U2 | |
| | 11 | B2 | 33 | F22 | 55 | Mi28 | 77 | UH60 | |
| | 12 | B52 | 34 | F35 | 56 | Mi8 | 78 | US2 | |
| | 13 | Be200 | 35 | F4 | 57 | Mig29 | 79 | V22 | |
| | 14 | C1 | 36 | FCK1 | 58 | Mig31 | 80 | Vulcan | |
| | 15 | C130 | 37 | H6 | 59 | Mirage2000 | 81 | WZ7 | |
| | 16 | C17 | 38 | Il76 | 60 | P3 | 82 | X32 | |
| | 17 | C2 | 39 | J10 | 61 | RQ4 | 83 | XB70 | |
| | 18 | C390 | 40 | J20 | 62 | Rafale | 84 | Y20 | |
| | 19 | C5 | 41 | J35 | 63 | SR71 | 85 | YF23 | |
| | 20 | CH47 | 42 | J36 | 64 | Su24 | 86 | Z10 | |
| | 21 | CH53 | 43 | JAS39 | 65 | Su25 | 87 | Z19 | |
|
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| --- |
|
|
| ## 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 |
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|
