Swarm Intelligence with Deep Transfer Learning Driven Aerial Image Classification Model on UAV Networks

Abstract

Nowadays, unmanned aerial vehicles (UAVs) have gradually attracted the attention of many academicians and researchers. The UAV has been found to be useful in variety of applications, such as disaster management, intelligent transportation system, wildlife monitoring, and surveillance. In UAV aerial images, learning effectual image representation was central to scene classifier method. The previous approach to the scene classification method depends on feature coding models with lower-level handcrafted features or unsupervised feature learning. The emergence of convolutional neural network (CNN) is developing image classification techniques more effectively. Due to the limited resource in UAVs, it can be difficult to fine-tune the hyperparameter and the trade-offs amongst computation complexity and classifier results. This article focuses on the design of swarm intelligence with deep transfer learning driven aerial image classification (SIDTLD-AIC) model on UAV networks. The presented SIDTLD-AIC model involves the proper identification and classification of images into distinct kinds. For accomplishing this, the presented SIDTLD-AIC model follows a feature extraction module using RetinaNet model in which the hyperparameter optimization process is performed by the use of salp swarm algorithm (SSA). In addition, a cascaded long short term memory (CLSTM) model is executed for classifying the aerial images. At last, seeker optimization algorithm (SOA) is applied as a hyperparameter optimizer of the CLSTM model and thereby results in enhanced classification accuracy. To assure the better performance of the SIDTLD-AIC model, a wide range of simulations are implemented and the outcomes are investigated in many aspects. The comparative study reported the better performance of the SIDTLD-AIC model over recent approaches.

Multi-Label Scene Classification on Remote Sensing Imagery Using Modified Dingo Optimizer With Deep Learning

“…In Table 4 and Fig. 9, the experimental validation of the MSCRSI-MDODL technique with recent models is made [23]. The results highlighted that the SIDTLD+SSA and DL-CaffeNet models have obtained worse performance.…”

AI Driven Approaches in Swarm Robotics - A review

“…This concise overview facilitates a comprehensive understanding of the diverse applications of AI in swarm robotics, alongside the testing environments and specific methodologies employed across the studies. -√ Large language model (LLM) [21] -√ RL algorithm [5] √ -Dueling Double Deep Q-Network (D3QN) [6] √ -Deep Learning Trained by Genetic Algorithm (DL-GA) [8] √ -3D StringNet herding [10] √ -Decision-making mechanisms [12] √ -Deep Imitation Reinforcement Learning (DIRL) [17] Augmented Lagrangian particle swarm optimization (ALPSO) [20] √ √ Automatic modular design approach (AutoMoDe) [24] Coordination -√ AudioLocNetv(deep learning module) [31] √ -Not specified [32] √ -End-to-end Neural Networks to train robots [27] √ -Mean-field feedback control [28] √ -Deep Neural Network (DNN) model [29] √ -variant of the crawling probabilistic road map motion planning algorithm [33] √ -distributed online reinforcement learning method [34] √ -coordination algorithm [51] Optimization -√ PSO algorithm [53] -√ streamlined algorithms [36] √ -Genetic algorithm (GA) [46] √ -Particle Swarm Optimization (PSO) [49] √ -Robot Bean Optimization Algorithm (RBOA) [50] √ -Automatic modular design method: AutoMoDe-Cedrata and AutoMoDe-Maple [52] √ -PPO algorithm [54] √ -Dijkstra algorithm [55] √ -WC and WET algorithms [44] √ √ Decentralized ergodic planning [35] Optimization and Navigation √ -YOLOv8 [41] √ -Quantum-based path-planning algorithm and Grover's search algorithm [42] √ -Genetic algorithms (GA) and Cellular automata techniques [9] √ -Mean-Field Control (MFC), deep reinforcement learning (RL), and collision avoidance algorithms [22] Optimization and Coordination √ -Knowledge-Based Neural Ordinary Differential Equations (KNODE) [23] √ -Surrogate models ...…”

Special Issue on Unmanned Aerial Vehicles

“…In [6], a light-weight deep neural network architecture is proposed for real-time object classification, considering mission specific input data augmentation techniques. In [7], a classifier is designed for aerial images via deep transfer learning for UAV networks.…”