Brief Deep Learning-Based ATR from UAV Imagery

Develop a CNN/Transformer model to detect and classify military targets (vehicles, personnel, threats) from UAV imagery. The model must handle small targets at high altitude, occlusion, and cluttered backgrounds. It should be robust to day/night variations, weather changes, and seasonal differences, and leverage multi-modal data (RGB, IR, thermal) to detect camouflaged or adversarial targets. Use semi-supervised or transfer learning to address limited labeled datasets. Optimize the model for real-time UAV deployment using lightweight architectures, pruning, or quantization. Include precision, recall, F1-score, and inference speed metrics, along with visualizations for operator interpretability. Document all methods, datasets, and configurations clearly for reproducibility and potential journal submission.

Expected Outcomes & Deliverables:

1. Fully trained and optimized ATR model capable of real-time UAV inference.
2. Evaluation report with performance metrics (precision, recall, F1-score, inference speed) under varied environmental conditions.
3. Sample detection visualizations and example scenarios demonstrating small, occluded, and camouflaged target recognition.
4. Complete documentation of datasets, model architecture, training procedures, and code for reproducibility.