Abstract
To detect the categories and positions of various transformer components in inspection images automatically, this paper proposes a transformer component detection model with high detection accuracy, based on the structure of Faster R-CNN. In consideration of the significant difference in component sizes, double feature maps are used to adapt to the size change, by adjusting two weights dynamically according to the object size. Moreover, different from the detection of ordinary objects, there is abundant useful information contained in the relative positions between components. Thus, the relative position features are defined and introduced to the refinement of the detection results. Then, the training process and detection process are proposed specifically for the improved model. Finally, an experiment is given to compare the accuracy and efficiency of the improved model and the original Faster R-CNN, along with other object detection models. Results show that the improved model has an obvious advantage in accuracy, and the efficiency is significantly higher than that of manual detection, which suggests that the model is suitable for practical engineering applications.
Highlights
With the popularization of inspection robots and the accumulation of image data in smart substations, the automatic recognition of power equipment states based on inspection images is increasingly being widely used, such as switch state recognition and insulator breakage identification [1,2,3]
The results show that the improved Faster R-CNN has an obvious increase in accuracy, which can satisfy the requirements for defect and fault recognition
According to the experiments in [11], for the neural network of improved Faster R-CNN model (FRCNN-improved, for short), the number of proposals selected into the category and position detection module (i.e., n1 ) is set to 2000 and 300 in the training and test process respectively, and the anchors in region proposal network (RPN) are of 4 sizes (82, 322, 1282, 5122 ) and 3 aspect ratios (2:1, 1:1, 1:2)
Summary
With the popularization of inspection robots and the accumulation of image data in smart substations, the automatic recognition of power equipment states based on inspection images is increasingly being widely used, such as switch state recognition and insulator breakage identification [1,2,3]. Traditional detection methods for power equipment and components are mainly rule-based algorithms with low-level features (e.g., Scale Invariant Feature Transform (SIFT) [6], Histograms of Oriented Gradient (HOG) [7]). Unlike ordinary objects, there are relatively fixed relations between the positions of transformer components, while detection models for ordinary objects cannot make full use of the relative position information Focusing on these problems, this paper proposes an improved model based on Faster Region-based Convolutional Neural Network (Faster R-CNN) [11], which is a classical deep learning network with relatively high accuracy in object detection. The results show that the improved Faster R-CNN has an obvious increase in accuracy, which can satisfy the requirements for defect and fault recognition
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