Automatic Detection of Transformer Components in Inspection Images Based on Improved Faster R-CNN

Ziquan Liu and Huifang Wang
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Ziquan Liu: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Huifang Wang: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China

Energies, 2018, vol. 11, issue 12, 1-18

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.

Keywords: object detection; transformer component; Faster R-CNN; double feature maps; relative position features; random forests; image processing; computer vision (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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