基于转移学习的地面目标识别方法
Qiuzhan Zhou1, Jikang Hu1, Huinan Wu1
1College of Communication Engineering, Jilin University, Changchun 130012, China.
Sensors (Basel, Switzerland)
|January 25, 2025
概括
本研究引入了一种使用卷积神经网络 (CNN) 和转移学习的新移动地面目标识别算法. 改进后的系统提高了识别行人和车辆的准确性和环境适应性.
科学领域:
- 机器人和自动化机器人与自动化
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 移动地面目标识别系统对于监测关键区域至关重要.
- 基于振动传感器的系统提供了小尺寸和低功耗等优势.
- 现有的算法与远距离目标,环境适应性和准确性作斗争.
研究的目的:
- 开发一个改进的移动地面目标识别算法.
- 提高适应新环境的能力,提高识别准确性.
- 为了利用转移学习来实现可靠的目标识别.
主要方法:
- 利用卷积神经网络 (CNN) 来从振动信号中提取特征.
- 应用领域适应技术以提高环境稳定性.
- 集成转移学习以提高算法的适应性.
主要成果:
- 拟议的算法成功地从振动信号中识别了目标类型.
- 在识别准确度方面取得了显著的改进.
- 展示了有效地适应不同的环境条件.
结论:
- 基于CNN和转移学习的新算法为移动地面目标识别提供了卓越的性能.
- 该方法提高了适应性和准确性,克服了现有方法的局限性.
- 这项工作为更先进的多模式融合和转移学习识别系统提供了基础.
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