DCDW-YOLOv11:用于关键输电线设备的智能故障检测方法
Dezhi Wang1, Riqing Song1, Minghui Liu1
1State Grid Liaoning Electric Power Co., Ltd., Dandong 118000, China.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
这项研究介绍了DCDW-YOLOv11,这是传输线路的增强缺陷检测模型. 它通过解决背景干扰和特征变化,在复杂环境中显著提高了准确性和可靠性.
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:
- 在输电线设备中检测缺陷对于电网可靠性至关重要.
- 复杂的环境带来了诸如背景干扰和多尺度特征等挑战,降低了准确性.
- 现有的方法在现实场景中难以获得准确性和稳定性.
研究的目的:
- 开发一款改进的传输线设备故障检测模型.
- 在复杂的环境条件下提高准确性和可靠性.
- 为智能输电线路检查提供强大的解决方案.
主要方法:
- 拟议的DCDW-YOLOv11模型是基于YOLOv11.11的.
- 内置可变形卷积C2f_DCNv3用于几何适应性.
- 集成卷积块注意模块 (CBAM) 用于特征突出显示和噪声抑制.
- 使用动态头 (DyHead) 进行跨层特征融合和WIoU损失以优化回归.
主要成果:
- DCDW-YOLOv11在传输线设备缺陷数据集上实现了94.4%的准确性,92.8%的回忆率和96.3%的mAP.
- 与原始YOLOv11相比,显著改善 (2.8%的准确率,7.0%的回忆率,4.4%的mAP).
- 在性能方面表现优于其他主流检测模型.
结论:
- DCDW-YOLOv11型号提供高精度和可靠的缺陷检测功能.
- 提议的改进有效地应对复杂环境中的挑战.
- 该方法支持对输电线路进行智能检查,提高运营安全性和效率.
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