I would like to express my gratitude to the following resources and communities that made this project possible.
This project utilizes the Military Aircraft Detection Dataset, which was instrumental in training and evaluating the deep learning models.
- Dataset Source: Kaggle - Military Aircraft Detection Dataset
- Original Data Provider: a2015003713 (Kaggle User)
- Original Repository: GitHub - Military Aircraft Detection
- Context: A comprehensive collection of military aircraft images covering various classes (Fighters, Bombers, Transports, etc.) used for classification tasks.
This project relies on the open-source ecosystem:
- TensorFlow & Keras: For model architecture and training.
- Streamlit: For building the interactive Tactical Dashboard.
- OpenCV: For image processing and HUD visualization.
- Plotly: For interactive data visualization.
- Institution: Universitas Muhammadiyah Malang (UMM)
- Department: Informatics Engineering
- Course: Machine Learning (Final Practicum Assignment)
If you use this code, data analysis, or the Tactical Dashboard in your research or project, please cite this repository as follows:
Ardiyansyah, B. (2025). Military Aircraft Detection: Comparative Framework of CNN, MobileNetV2, and EfficientNetB0 [Source code]. GitHub. https://github.com/RazerArdi/Military-Aircraft-Detection
@software{Ardiyansyah_Military_Aircraft_Detection_2025,
author = {Ardiyansyah, Bayu},
month = {12},
title = {{Military Aircraft Detection: A Comparative Framework}},
url = {[https://github.com/RazerArdi/Military-Aircraft-Detection](https://github.com/RazerArdi/Military-Aircraft-Detection)},
version = {1.0.0},
year = {2025},
publisher = {GitHub}
}