基于波形变换和渐进特征金字塔网络的高效目标检测方法:电网检查的案例研究
Ji Ye1, Bing Yuqi2, Wang Wendi3
1Nanjing Suyi Industrial Co., Ltd., Nanjing, China. 2524679177@qq.com.
Scientific reports
|February 4, 2026
概括
这项研究增强了YOLOv11在高压输电线路上检测异物,提高了准确性和效率. 新型号能更好地检测小,低对比度的物体,这对于电网安全至关重要.
科学领域:
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
- 电气工程 电气工程
背景情况:
- 高压输电线路对于经济和公共福利至关重要.
- 外来物体对电力系统的稳定性和安全性构成重大风险.
- 现有的物体检测模型在复杂的环境中与小的,低对比度的外来物体作斗争.
研究的目的:
- 开发一个增强的YOLOv11对象检测模型,用于高压输电线路检查.
- 为了提高外来物体的检测准确度和稳定性,特别是小型和低对比度的外来物体.
- 为电网检查场景提供实用,实时的解决方案.
主要方法:
- 集成波形变形卷积 (WTConv) 块,以增强特征分解和细节保存.
- 开发了一种渐进特征金字塔网络 (PFPN) 用于多级特征融合和精炼.
- 引入了内部EIoU损失功能,以专注于精确定位小目标.
主要成果:
- 改进的探测器在TLFO数据集上改善了mAP0.5从0.841到0.872和mAP0.5:0.95从0.620到0.640.
- 实现了更高的精度 (0.962 vs 0.918),降低了参数 (4.83M vs 5.97M),并增加了推断速度 (28.5 FPS vs 24.1 FPS).
- 证明了可通用性,在MS COCO数据集上改善了+1.6点mAP0.5:0.95.
结论:
- 拟议的WTConv,PFPN和Inner-EIoU组合有效地解决了传统外来物体检测模型的局限性.
- 增强的YOLOv11为实时电网检查提供了实用和高效的解决方案.
- 该模型在MS COCO上的表现表明其在电网领域之外的潜在适用性.
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