基于深度学习辅助的无人机图像草原犬优化算法在无人机影像空中图像分类中的应用

Prairie Dog Optimization Algorithm with deep learning assisted based Aerial Image Classification on UAV imagery.

作者信息

K Alkhalifa Amal, Kashif Saeed Muhammad, M Othman Kamal, A Ebad Shouki, Alonazi Mohammed, Mohamed Abdullah

机构信息

Department of Computer Science and Information Technology, Applied College, Princess Nourah bint Abdulrahman University, P.O.Box 84428, Riyadh, 11671, Saudi Arabia.

Department of Computer Science, Applied College at Mahayil, King Khalid University, Saudi Arabia.

出版信息

Heliyon. 2024 Sep 7;10(18):e37446. doi: 10.1016/j.heliyon.2024.e37446. eCollection 2024 Sep 30.

DOI:10.1016/j.heliyon.2024.e37446

PMID:39309890

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11416545/

Abstract

This study presents a Prairie Dog Optimization Algorithm with a Deep learning-assisted Aerial Image Classification Approach (PDODL-AICA) on UAV images. The PDODL-AICA technique exploits the optimal DL model for classifying aerial images into numerous classes. In the presented PDODL-AICA technique, the feature extraction procedure is executed using the EfficientNetB7 model. Besides, the hyperparameter tuning of the EfficientNetB7 technique uses the PDO model. The PDODL-AICA technique uses a convolutional variational autoencoder (CVAE) model to detect and classify aerial images. The performance study of the PDODL-AICA model is implemented on a benchmark UAV image dataset. The experimental values inferred the authority of the PDODL-AICA approach over recent models in terms of dissimilar measures.

摘要

本研究提出了一种基于深度学习辅助的无人机图像分类方法的草原犬优化算法（PDODL-AICA）。PDODL-AICA技术利用最优的深度学习模型将无人机图像分类为多个类别。在所提出的PDODL-AICA技术中，特征提取过程使用EfficientNetB7模型执行。此外，EfficientNetB7技术的超参数调整使用草原犬优化（PDO）模型。PDODL-AICA技术使用卷积变分自编码器（CVAE）模型来检测和分类无人机图像。PDODL-AICA模型的性能研究在一个基准无人机图像数据集上进行。实验值表明，在不同度量方面，PDODL-AICA方法优于近期的模型。