传输线的异物检测算法基于加权的空间注意力
Yuanyuan Wang1, Haiyang Tian1, Tongtong Yin1
1School of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, Jiangsu, China.
Frontiers in neurorobotics
|July 19, 2024
概括
一个新的加权空间注意 (WSA) 网络准确地检测电线上的异物,提高了检测率3%至97.6%. 这种先进的系统提高了电力传输基础设施的安全性和可靠性.
科学领域:
- 电气工程 电气工程
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 电力输电线路的安全运行至关重要.
- 外部因素,如塑料薄膜和风构成风险,导致潜在的停电.
- 现有的检测方法是低效的,在复杂的环境中缺乏准确性.
研究的目的:
- 开发一种精确的自动化系统,用于检测电线上的异物.
- 为了解决当前方法的局限性,特别是背景纹理阻塞.
- 加强电力传输基础设施的监控和维护.
主要方法:
- 引入了一个权重空间注意力 (WSA) 网络模型.
- 采用先进的图像预处理:色彩空间转换,图像增强和大选择性内核网络 (LSKNet).
- 使用动态稀疏的双级空间注意模块 (BSAM) 进行特征提取,以及优化的双向特征金字塔网络 (BiFPN) 进行特征融合.
主要成果:
- 在电力线 (PL) 数据集上,WSA模型实现了97.6%的测试识别精度.
- 与YOLOv8模型相比,在准确度方面表现出了3个百分点的改善.
- 在复杂的环境背景下,在检测外来物体方面表现出卓越的能力.
结论:
- 先进预处理,BSAM和BiFPN的综合方法有效地提高了检测准确度.
- WSA模型为识别电力线路上的异物材料提供了显著的改进.
- 这项技术有可能彻底改变输电基础设施的监控和维护.
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