The document discusses a deep learning model for military aircraft detection. It introduces the topic and importance of using machine learning for aircraft surveillance. The model is trained on a dataset of 43 aircraft types using images with bounding box annotations. The code preprocesses the data, defines a generic convolutional neural network model, trains it for 5 epochs, and evaluates the results. The trained model achieves 99.35% accuracy on the training set and 90.34% on the validation set, demonstrating strong performance while also showing signs of potential overfitting.

1. Topic: Military Aircraft Detection using Deep Learning model.
Introduction:
Military Aircraft Detection using machine learning is a cutting-edge technology that aims to
enhance the surveillance and security systems in the aerospace domain. By leveraging advanced
algorithms and neural networks, this approach enables the automatic identification and
classification of military aircraft from various sources such as radar data, satellite imagery, and
sensor networks. The application of machine learning techniques facilitates real-time
monitoring, early warning systems, and precise threat assessment, significantly bolstering
defense capabilities. With the ability to accurately detect military aircraft and distinguish them
from civilian ones, this innovative solution plays a crucial role in safeguarding national airspace,
ensuring territorial integrity, and supporting strategic decision-making in military operations.
Dataset Description:
- Dataset Name: Military Aircraft Detection Dataset
- Dataset Link:
[https://www.kaggle.com/datasets/a2015003713/militaryaircraftdetectiondataset]
- Bounding Box Format: PASCAL VOC format (xmin, ymin, xmax, ymax)
- Aircraft Types: 43 different aircraft types included in the dataset
Code:
1. The code imports necessary libraries and modules for data handling, deep learning, and
visualization, and ignores warnings.

2. 2. The functions "define_paths" and "define_df" generate data paths with labels and
concatenate them into a dataframe for model training.
3. The functions "tr_ts_data", "full_data", and "tr_val_ts_data" generate train, validation, and
test dataframes by utilizing the "define_paths" and "define_df" functions for various data
directory configurations.

3. 4. Function to split data into train, valid, test

4. 5. Function to generate images from dataframe

5. 6. Function to display data sample

6. 7. Function to plot value counts for a column in a dataframe

7. 8. Callbacks : Helpful functions to help optimize model training Examples: stop model training
after specfic time, stop training if no improve in accuracy and so on.

11. 9. Function to plot history of training

12. 10. Function to create Confusion Matrix

13. 11. Start Reading Dataset

14. 12. Generic Model Creation

15. 13. Set Callback Parameters

16. 14. Train Model
15. Display model performance
16. Evaluate model

17. 17. Get Predictions
18. Confusion Matrics and Classification Report

18. 19. Save Model
20. Generate CSV files containing classes indicies & image size

19. Result Analysis:
The training of the Military Aircraft Detection model was halted at epoch 5 as per the user's
input. The training duration was approximately 19 minutes and 55.41 seconds.
The model achieved impressive performance on the training set with a loss of 0.7014 and an
accuracy of 99.35%. This indicates that the model was able to effectively learn the patterns
and features of the training data.
On the validation set, the model achieved a slightly higher loss of 1.0375 and an accuracy of
90.34%. This suggests that the model performed well on unseen data, although there is a
slight increase in loss compared to the training set.
Conclusion:
Overall, the model demonstrates strong performance in detecting military aircraft, achieving
high accuracy on both the training and validation sets. However, there is a slight drop in
performance on the test set, which may suggest the presence of some degree of overfitting.
Further analysis and fine-tuning of the model could be done to enhance its generalization
capabilities.