基于HSV特征变换与目标检测的变压器呼吸器缺陷智能识别方法

Intelligent Defect Identification Method of Transformer Dehumidifier Based on HSV Transformation and Object Detection

为了利用深度学习算法提高变压器运维的智能化水平,提出了一种基于色相饱和度值(hue-saturation-value,HSV)特征变换与目标检测的变压器呼吸器缺陷智能识别方法。该方法利用单发多盒探测器(single shot multibox detector,SSD)网络框架进行呼吸器目标提取,采用HSV颜色转换完成空间映射,通过设定HSV特征阈值进行呼吸器正常颜色和异常颜色的跟踪和提取,进而通过各颜色分量比例与分布情况进行呼吸器状态的智能判断。研究结果表明:所提识别方法能够利用图像特征对变压器呼吸器进行准确定位与状态识别。论文研究可为电力设备锈蚀识别等其他类似场景提供参考。

In order to use the deep learning method to improve the intelligent level of transformer operation and maintenance,we proposed an intelligent recognition method of transformer dehumidifier defects based on hue-saturation-value(HSV)feature transformation and object detection.Firstly,the respirators are extracted by adopting single shot multibox detector(SSD)network,and the features are achieved through spatial mapping by HSV color transformation.Then,the normal and abnormal color components of dehumidifier are tracked and extracted by setting the HSV characteristic thresholds.Finally,the dehumidifier state is estimated by proportion and distribution of each color components.The research results show that the proposed recognition method can be adopted to accurately locate and identify the status of the transformer dehumidifier by using image features.The research can provide a reference for other similar scenes such as rust recognition of power equipment.