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基于轻量级网络和类似残余的跨层特征融合的电力线路细分算法.
Wenqiang Zhu1, Huarong Ding1, Gujing Han1
1School of Electronic and Electrical Engineering, Wuhan Textile University, Wuhan 430200, China.
Sensors (Basel, Switzerland)
|September 19, 2025
概括
本研究介绍了RGS-UNet,这是一种轻量级的深度学习模型,用于无人机检查中的电力线路细分. 它提高了准确性并降低了参数,使其成为实时边缘部署的理想选择.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 电气工程 电气工程
背景情况:
- 电力线路细分对于安全的无人机检查至关重要.
- 现有的深度学习模型面临着小目标,复杂的背景和大量参数数量的挑战.
研究的目的:
- 开发一个轻量级和准确的深度学习模型用于电力线路细分.
- 解决无人机检查场景中当前算法的局限性.
主要方法:
- 推出了RGS-UNet,这是一种轻量级的细分模型,具有修改后的UNet骨干 (ResNet18) 和幽灵模块优化.
- 整合了一个SIMAM注意力机制,通过类似残留的添加来增强特征提取.
- 利用Mish激活功能来保持准确性并防止过度拟合.
主要成果:
- 与经典的UNet相比,RGS-UNet在F1-Score和IoU方面实现了2.05%和2.58%的改进.
- 模型的参数数量减少到原来的UNet的57.25%.
- 在准确性和轻量化方面都表现出卓越的性能.
结论:
- 在无人机检查中,RGS-UNet为电力线路细分提供了有效的解决方案.
- 该模型的轻量化性质和提高的准确性使其适用于边缘部署.
- 这项研究有助于更安全,更有效地监控输电线路.
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