The KIIT Military Target Archive (KIIT-MiTA) dataset is a high-resolution drone-captured image dataset designed for military object detection and recognition. It provides carefully annotated images in the YOLO format, enabling researchers and developers to train models for real-time surveillance, object detection, and tracking in military applications.

• Total Images: 1,700
• Annotation Format: YOLO
• Total Annotations: 4,100+
• Classes: 7 distinct military objects
• Resolution: High-resolution drone imagery
• Sources: Publicly available data, YouTube frames, and self-collected images

The dataset includes 7 military object categories:

1. Artillery
2. Missile
3. Radar
4. M. Rocket Launcher
5. Soldier
6. Tank
7. Vehicle

Each image is labeled with bounding boxes and class annotations, ensuring precise object detection capabilities.

You can access the dataset from multiple sources:

• 📌 Kaggle: KIIT-MiTA Dataset
• 📌 Google Drive: Download Here
• 📌 Mega: Mega Download

• Collected from publicly available military datasets, YouTube video frames, and self-captured drone images.
• Covers various lighting conditions, weather scenarios, and terrains to enhance model generalization.

• Labeled using CVAT (Computer Vision Annotation Tool).
• 4,100+ object annotations were manually reviewed for accuracy.
• Each annotation includes bounding box coordinates and class labels, normalized for YOLO model compatibility.

• 📸 High-resolution drone imagery for precise military object detection
• 🏗 Manually annotated dataset optimized for deep learning models
• 🔄 Robust augmentation techniques applied for better generalization
• 🏆 Split into Training (80%), Validation (10%), and Testing (10%)
• 💡 Ideal for YOLO, Faster R-CNN, SSD, and other object detection models

• ✅ Educational & Research Use Only
• 🚫 Strictly Prohibited for Commercial Use
• 🔗 Must credit the authors if used in research/publications

If you use this dataset in your research, please cite our work and provide proper attribution.

This dataset was created by researchers from KIIT University:

👨‍🏫 Dr. Rajdeep Chatterjee
Associate Professor, School of Computer Engineering, KIIT
🔗 LinkedIn

This dataset is available under the Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

🔗 More details: CC BY-NC 4.0 License

If you use this dataset, please cite our research paper:

@INPROCEEDINGS{10969335,
  author={Chakrabarty, Sudip and Chatterjee, Rajdeep and Chakraborty, Sorup and Roy Shuvo, Sourov and Chowdhury, Rajesh},
  booktitle={2025 3rd International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC)}, 
  title={Drones in Defense: Real-Time Vision-Based Military Target Surveillance and Tracking}, 
  year={2025},
  volume={},
  number={},
  pages={508-513},
  keywords={Training;Target tracking;Accuracy;Military computing;Surveillance;Computational modeling;Radar tracking;Real-time systems;Drones;Videos;Drone;KIIT-MiTA;Military;Object Detection;YOLOv11;Tracking},
  doi={10.1109/ISACC65211.2025.10969335}}


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## 🛠 Recommended Usage
This dataset is **ideal for training deep learning-based object detection models** such as:
- **YOLOv4, YOLOv5, and YOLOv8**
- **Faster R-CNN**
- **SSD (Single Shot MultiBox Detector)**
- **EfficientDet**
- **Vision Transformers (ViTs)**

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## 📬 Contact
For any questions or contributions, feel free to reach out via email or connect on LinkedIn.

📧 **Email:** rajesh99.bd@gmail.com
📧 **Email:** sudipchakrabarty6@gmail.com  
 
🔗 **GitHub Repo:** [KIIT-MiTA](https://github.com/Sudip-329/KIIT-MiTA)

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## ⭐ Acknowledgment
We appreciate the support of **KIIT University** and **Dr. Rajdeep Chatterjee** sir for facilitating this research and providing computational resources for dataset preparation.

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