Authors:
Yanjun Dong
1
;
Shigeng Wang
2
;
Xiaoyu Yin
1
;
Xi Chen
1
and
Jiao Peng
1
Affiliations:
1
State Grid Hebei Electric Power Company Co., Ltd Information & Telecommunication Branch, Shijiazhuang, China
;
2
Beijing University of Posts and Telecommunications, Beijing, China
Keyword(s):
Power Technology, Small Sample, Target Detection.
Abstract:
In view of the difficulties, low efficiency and large amount of data in current grid patrol inspection, this project plans to study a small sample patrol inspection method based on dual-core. Firstly, based on the object recognition method of FASTERRCNN, a two-person network model of image and query image is constructed. Then, the improved regional proposal network (RPN) module is used to generate a higher quality proposal; finally, the regional boundary of the supporting image and the query image is matched to a new regional boundary. The experiments show that the method can detect the“Bird’s nest” and“Insulator” in the power network with only 10 support maps in the EPD database established by ourselves, its detection index mAP value can reach 18.92% . Compared with other algorithms, the detection model of small sample based on binary star network proposed in this project has better performance and greater lightweight advantage under the condition of small sample, it can provide ref
erence for the research of new power detection methods.
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