草原犬优化算法与深度学习辅助基于无人机图像的空中图像分类
Amal K Alkhalifa1, Muhammad Kashif Saeed2, Kamal M Othman3
1Department of Computer Science and Information Technology, Applied College, Princess Nourah bint Abdulrahman University, P.O.Box 84428, Riyadh, 11671, Saudi Arabia.
Heliyon
|September 23, 2024
概括
本研究介绍了一种新的草原狗优化算法,用于无人机图像的深度学习辅助空中图像分类 (PDODL-AICA). 采用PDODL-AICA方法,使用EfficientNetB7和卷积变异自编码器,提高了空中图像分类的准确性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 遥感 遥感 遥感 遥感
背景情况:
- 空中图像分类对于各种应用,包括环境监测和城市规划至关重要.
- 深度学习模型为图像分析提供了强大的功能,但需要高效的优化和特征提取.
- 无人驾驶飞行器 (UAV) 图像由于规模,分辨率和各种环境条件而存在独特的挑战.
研究的目的:
- 为无人机图像开发和评估一种新的深度学习辅助的空中图像分类方法.
- 为了优化深度学习模型的性能,使用元启发算法对空中图像进行分类.
- 为了提高探测和分类多个类别的航空图像的准确性和效率.
主要方法:
- 拟议的草原狗优化算法与深度学习辅助的空中图像分类 (PDODL-AICA) 方法.
- 使用EfficientNetB7模型从无人机图像中提取特征.
- 使用草原犬优化 (PDO) 算法对EfficientNetB7.7.的超参数调整进行调整.
- 实施一个卷积变量自编码器 (CVAE) 用于空中图像检测和分类.
主要成果:
- PDODL-AICA模型在基准无人机图像数据集上表现出卓越的性能.
- 实验结果表明,与现有方法相比,分类准确度有了显著的改善.
- PDO算法有效地优化了EfficientNetB7模型的超参数,提高了其分类能力.
结论:
- PDODL-AICA方法为使用无人机数据进行空中图像分类提供了强大而有效的解决方案.
- 将元启发式优化与深度学习模型的整合显著提高了分类性能.
- 这项研究强调了PDODL-AICA在先进的空中图像分析应用中的潜力.
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