International Journal of Advances in Applied Computational Intelligence
Journal DOI
2833-5600ISSN (Online)
Volume 6 , Issue 1 , PP: 1-12, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Sanjar Mirzaliev 1 , Samandarboy Sulaym 2
Doi: https://doi.org/10.54216/IJAACI.060101
Unmanned Aerial Vehicles (UAVs), together with Internet of Things (IoT) technology, have emerged as robust tools for remote sensing (RS) and data collection in different sectors, including environmental monitoring, agriculture, and disaster management. The incorporation of data from UAVs with IoT sensors on the ground can provide a holistic view of the environment, improving the quality of input for image classification. Deep learning (DL) models-based image classification is a key component of IoT-assisted UAVs, transforming them from data collection tools into intelligent decision-making platforms. Especially, Convolutional Neural Networks (CNNs) can automatically recognize objects, patterns, and anomalies in images captured by UAVs. Therefore, the study presents an automated image classification with the Tyrannosaurus optimization algorithm using deep learning (AIR-TROADL) method on the IoT-aided UAV network. The AIR-TROADL technique aims to examine the UAV images for the identification and classification of images into distinct categories. In the projected AIR-TROADL method, an enhanced ShuffleNet model is exploited for feature extraction. Besides, the hyperparameter tuning of enhanced ShuffleNet model can be performed by using TROA, which in turn boosts the classification performance. Finally, the classification of images takes place using the attention-based gated recurrent unit (AGRU) model. A series of simulations have been conducted to exhibit the promising outcome of the AIR-TROADL technique. The comparative outcomes highlighted that the AIR-TROADL method reaches high efficiency over its recent approaches in terms of distinct measures.
Unmanned aerial vehicles , Image classification , Remote sensing , Deep learning , Computer vision
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