基于增强特征金字塔和可变形卷积的绝缘子缺陷检测

绝缘子广泛应用于电力系统的各个环节,对保障电网安全稳定运行起到重要作用。现有方法只能识别自爆缺失、异物等明显缺陷,无法应对局部破损、裂纹等情况。针对上述问题,提出一种基于增强特征金字塔和可变形卷积的绝缘子缺陷检测方法:在原有高、低特征融合的基础上,增加增强的自底而上的路径,改善高、低特征图之间的信息传递,实现局部缺陷特征的有效提取;引入可变形卷积,自适应改变局部采样点,减小背景干扰的影响,进一步提升模型的适用性。利用多场景采集的绝缘子图像进行对比实验,结果显示在不同基础网络上,所提方法检测精度较传统方案均取得了较大程度的提升,该方法可广泛应用于变电站、高压输电线等各类绝缘子应用场景。

Insulator defect detection based on enhanced feature pyramid and deformable convolution

Insulators which are widely used in all aspects of the power system play an important role in ensuring the safe and stable operation of the power grid. Existing methods are only able to identify obvious defects such as self-explosion missing and foreign objects, and cannot deal with local damage, cracks and other situations. In response to the above problems, an insulator defect detection method based on enhanced feature pyramid and deformable convolution is proposed. On the basis of the original high and low feature fusion, an enhanced bottom-up path is added, which improves the information transfer between high and low feature maps, and realizes the effective extraction of local defect features. The introduction of deformable convolution, adaptively changes local sampling points, reduces the impact of background interference, and further improves the applicability of the model. Comparative experiments using insulator images collect in multiple scenes show that the proposed method achieves greater detection accuracy improvements on different basic networks, and can be widely used in various insulator application scenarios such as substations and high-voltage transmission lines.