PBZGNet:基于逐步并行分支架构的变电站设备的新型缺陷检测网络
Mintao Hu1,2, Yang Zhuang2,3, Jiahao Wang2
1College of Computer and Information Engineering (College of Artificial Intelligence), Nanjing Tech University, Nanjing 211816, China.
Sensors (Basel, Switzerland)
|January 10, 2026
概括
通过改进小目标识别和精确定位,PBZGNet增强了变电站设备缺陷检测. 这种新网络的性能明显优于YOLOv11和YOLO-SD等现有模型,提高了电网可靠性.
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:
- 变电站设备的运行对于电网可靠性至关重要.
- 现有的深度学习探测器面临着小目标,多尺度特征和在混乱的变电站场景中精确定位的挑战.
研究的目的:
- 引入PBZGNet,这是一个新的故障检测网络,旨在克服当前变电站设备监控的局限性.
- 为了提高复杂的变电站环境中故障的检测精度和定位精度.
主要方法:
- 开发了PBZGNet,其中包括一个渐进式并行分支的骨干 (BiCoreNet),一个变焦融合子 (ZFusion,Concat-CBFuse) 和一个全局通道重新校准模块.
- 集成了一个以注意力为导向的脱探测头 (ADHead),并使用了通用焦点损失与质量评级方案和分布回归.
主要成果:
- 与YOLOv11相比,PBZGNet在所有尺度上都表现出优异的性能.
- 轻量级的PBZGNet-n实现了83.9%的mAP@50与2.91M参数,超过YOLOv11-n的9.3%.
- 完整的PBZGNet超过了YOLO-SD模型的7.3%mAP@50,建立了一个新的最先进的状态.
结论:
- PBZGNet有效地解决了小目标检测,多尺度特征融合和变电站缺陷检测中精确定位的挑战.
- 拟议的网络在变电站设备缺陷检测方面设置了一个新的最先进的技术,提高了整体电网可靠性.
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