Aircraft Classification from Aerial Imagery.

Completed:
.

This project tackles the challenge of identifying military aircraft from aerial images using computer vision techniques. We created a labeled aircraft dataset with RoboFlow and applied data augmentation to enhance model performance. Three models were evaluated: YOLOv5, YOLOv8 (by Ultralytics), and a custom ConvNet built with PyTorch. The custom model aimed to balance computational efficiency and detection accuracy, addressing shortcomings of the baseline models. By comparing these models, we identified performance trade-offs and gained insights into optimizing aircraft detection for realistic datasets.



This project is the result of a semester project completed in Fall 2024 for the University of Central Florida. See the university website here.


The project repository can be located using this provided link. It contains the project composition, repository navigation, models' information, and evaluations completed. Click here.




▱▰▱ Project Goals. ▰▱▰

  • 1. Create a labeled dataset of military aircraft using RoboFlow to train and evaluate computer vision models.
  • 2. Compare the performance of YOLOv5, YOLOv8, and a custom deep learning model (ConvNet) for aircraft detection.
  • 3. Design a custom ConvNet to balance detection accuracy and computational efficiency, addressing issues found in baseline models.
  • 4. Use standard evaluation metrics (precision, recall, F1 score, and mAP) to assess model effectiveness and identify performance trade-offs.
  • 5. Gain insights into improving computer vision models for accurate aircraft detection in a challenging real-world dataset.

Or reach out to me via email. 📧
Ask me anything! Just let me know you came from my website. 😁
jkglaspey@gmail.com