一个改进的白优化器-衍生自适应多通道DeepLabv3+用于空中图像的语义细分
1School of Electronics Engineering, Vellore Institute of Technology, Vellore, India.
PloS one
|October 30, 2023
概括
这项研究引入了一个自适应式多通道Deeplabv3+ (AMC-Deeplabv3+) 模型,通过改进的白优化 (IBWO) 算法进行优化,以提高远程传感图像的语义细分精度.
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:
- 遥感图像的语义细分具有挑战性,原因是传统网络中图像大小不一致和空间特征丧失.
- 现有的深度学习模型在细分精度和保存目标规模信息方面扎.
研究的目的:
- 为了提高远程传感图像的语义细分的准确性和效率.
- 解决目前用于空中图像分析的深度学习技术的局限性.
主要方法:
- 开发了一个自适应多通道Deeplabv3+ (AMC-Deeplabv3+) 模型.
- 使用了改进的白优化 (IBWO) 算法来优化AMC-Deeplabv3+模型的超参数.
- 在两个数据集上评估模型的性能,测量精度和子系数.
主要成果:
- 在两个数据集上实现了98.65%和98.72%的高准确率.
- 对于各自的数据集,获得的子系数为98.73%和98.85%.
- 与CNN,MUnet和DFCNN等现有方法相比,表现出优异的性能.
结论:
- 拟议的AMC-Deeplabv3+模型与IBWO进行了优化,显著提高了远程传感图像的语义细分性能.
- 该模型有效地克服了与图像大小不一致和特征尺寸损失相关的挑战.
- 这种方法为准确和高效的航空图像分析提供了有前途的解决方案.
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