Underwater Aerial Vehicle Networks Based Image Analysis By Deep Learning Architecture Integrated With 5G System

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Aakansha Vyas
Anand Sharma


With its astonishing ability to learn representation from data, deep neural networks (DNNs) have made efficient advances in the processing of pictures, time series, spoken language, audio, video, and many other types of data.In an effort to compile the volume of information generated in remote sensing field's subfields, surveys and literature revisions explicitly concerning DNNs methods applications are carried out Aerial sensing research has recently been dominated by applications based on Unmanned Aerial Vehicles (UAVs).There hasn't yet been a literature review that integrates the "deep learning" and "UAV remote sensing" thematics.This research propose novel technique in underwater aerial vehicle networks based image analysis by feature extraction and classification utilizing DL methods. here UAV based images through on 5G module is collected and this image has been processed for noise removal, smoothening and normalization. The processed image features has been extracted using multilayer extreme learning based convolutional neural networks. Then extracted deep features has been classified utilizingrecursive elimination based radial basis function networks. The experimental analysis is carried out based on numerous UAV image dataset in terms of accuracy, precision, recall, F-measure, RMSE and MAP.Proposed method attained accuracy of 96%, precision of 94%, recall of 85%, F- measure of 72%, RMSE of 48%, MAP of 41%.

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Vyas, A. ., & Sharma, A. . (2022). Underwater Aerial Vehicle Networks Based Image Analysis By Deep Learning Architecture Integrated With 5G System. International Journal on Future Revolution in Computer Science &Amp; Communication Engineering, 8(3), 65–74. Retrieved from http://ijfrcsce.org/index.php/ijfrcsce/article/view/2092


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