Power Equipment Recognition Method based on Mask R-CNN and Bayesian Context Network

In order to improve the accuracy of power equipment recognition, an image recognition method based on Mask RCNN and Bayesian Context Network is proposed. The two-layer network contains Mask R-CNN as the first layer, which gives preliminary recognition results, and Bayesian Context Network as the sec...

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Bibliographic Details
Published in:	IEEE Power & Energy Society General Meeting pp. 1 - 5
Main Authors:	Siheng, XIONG, Yadong, LIU, Rui, XU, Ying, DU, Zihan, CONG, Yingjie, YAN, Xiuchen, JIANG
Format:	Conference Proceeding
Language:	English
Published:	IEEE 02.08.2020
Subjects:	Bayesian Context Network Mask R-CNN power equipment in substation power equipment recognition relationship between objects
ISSN:	1944-9933
Online Access:	Get full text
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Summary:	In order to improve the accuracy of power equipment recognition, an image recognition method based on Mask RCNN and Bayesian Context Network is proposed. The two-layer network contains Mask R-CNN as the first layer, which gives preliminary recognition results, and Bayesian Context Network as the second layer, which utilizes context information to correct the preliminary results. Bayesian Context Network is designed to take the relationship of type, size, and spatial location between objects into account. Experiments on images of power equipment in substation show that the proposed method outperforms Mask R-CNN, Multi-task Network Cascades and Fully Convolutional Instance-aware Semantic Segmentation. Since power equipment in a picture shows a strong correlation, the proposed method is quite suitable for power equipment recognition.
ISSN:	1944-9933
DOI:	10.1109/PESGM41954.2020.9281755