基于修改后的GhostNet的无人机遥感图像的实时场景分类
Xiaole Shen1, Hongfeng Wang1, Biyun Wei1
1College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China.
PloS one
|June 7, 2023
概括
一个修改后的GhostNet有效地对无人机 (UAV) 图像进行实时遥感分类. 这种轻量级网络显著降低了计算成本和内存使用量,同时提高了分类准确性.
科学领域:
- 计算机视觉 计算机视觉
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
背景情况:
- 无人机对于自主遥感图像分类至关重要.
- 在嵌入式系统上部署深度学习模型进行实时分析,面临资源限制.
研究的目的:
- 开发一个轻量级的深度学习网络,用于高效的无人机遥感图像分类.
- 为了平衡嵌入式应用程序的计算效率和分类准确性.
主要方法:
- 一个新的轻量级网络,修改后的GhostNet,是基于GhostNet设计的.
- 网络修改包括改变卷积层,并用完全卷积层取代完全连接层.
- 对UCMerced,AID和NWPU-RESISC数据集的性能进行了评估.
主要成果:
- 修改后的GhostNet将浮点运算 (FLOP) 从7.85MFLOP减少到2.58MFLOP.
- 内存使用量从16.40 MB下降到5.70 MB,预测时间有18.86%的改善.
- 平均准确度在AID上增加了4.70%,在UCMerced数据集上增加了3.39%.
结论:
- 修改后的GhostNet在远程传感场景分类中为轻量级网络提供了更好的性能.
- 该网络有效地使用无人机实现实时地面场景监控.
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