A Comparative Evaluation of Drone Detection Models on Aerial Imageryacross Varying Training Epochs - Ayuningtyas, Astika; Riadi, Imam; Yudhana, Anton (JUITA: Jurnal Informatika, 2025)
Drone detection in aerial imagery has become increasingly important in security, surveillance, and military applications. This study aims to evaluate the performance of a deep learning model in detecting drone images by varying the number of training epochs (10, 20, and 50 epochs). A drone image dataset was used to train and test the model, with performance evaluated using precision, recall, mAP@0.5, and mAP@0.5:0.95 metrics. The experimental results indicate that increasing the number of epochs significantly enhances model performance. At 10 epochs, the model achieved a precision of 0.905, recall of 0.857, mAP@0.5 of 0.904, and mAP@0.5:0.95 of 0.455. At 20 epochs, recall improved to 0.879, and mAP@0.5:0.95 increased to 0.476. The best performance was observed at 50 epochs, with a precision of 0.918, recall of 0.886, mAP@0.5 of 0.920, and mAP@0.5:0.95 of 0.494. These findings demonstrate that increasing the number of training epochs not only improves detection accuracy but also enhances the model's generalization capability. The study concludes that training for 50 epochs is the optimal configuration for achieving the best performance in drone image detection, despite requiring longer training time. These results provide practical recommendations for implementing deep learning models in real-world drone detection applications.