Detection and identification drones using long short-term memory and Bayesian optimization

Abstract

This paper proposed a model based on bidirectional Long Short-Term Memory (Bi-LSTM) and Bayesian optimization to detect different drones in different Scenarios. Six different drones in three distinct scenarios—cloudy, sunny, and evening—are included in the dataset. The three primary stages of the suggested model are pre-processing, hyper-parameter optimization, and classification phase. Images are resized, noise is reduced, and contrast is enhanced in the first step. The hyperparameter values are then chosen in the second step using Bayesian optimization. In the end, the proposed model is constructed with nine layers based on the Gated Recurrent Unit (GRU) and Bi-LSTM for classification. For the cloudy scenario, the model achieves 97.43% accuracy, 99.52% sensitivity, 92.79% specificity, 96.64% precision, and 98.06 F1-score. In addition, the sunny scenario achieves 93.87%in accuracy, 97.14% in sensitivity, 87.06% in specificity, 94% in precision, and 90.25 in F1_score. The final experiment in the evening scenario is 97.50% accuracy, 99.39% sensitivity, 93.90% specificity, and 96.89% precision. Comparative results are presented at the end of the paper, and it shows that the proposed model overcomes previous works that used the same dataset.