A SEMANTIC SEGMENTATION GUIDED ABNORMALITY DETECTION METHOD FOR RESPIRATOR OIL LEVEL GAUGE
To tackle the scarcity of abnormal data in the substation for training deep neural networks,we particularly consider the identification of transformer respirator oil level gauge in this paper,and propose a semantic segmentation guided abnormality detection method with synthetic data.Specifically,an anomaly detection model integrating semantic segmentation and classifier was constructed.The semantic segmentation model could extract rich features with spatial semantic information of the input image,and meanwhile the classifier could predict accurately abnormal state category.Moreover,we proposed a training method integrating synthetic data and real data.Through adjusting the proportion of the two types of data,we could effectively train the model even with a small amount of real data.The experimental results show that the synthetic data can effectively alleviate the insufficient problem of real data,and the proposed method can achieve higher accuracy than directly using DCNN classification model.
万方数据
维普
