The Exploration of Small Sample-Oriented Object Detection Technology in the Field of Electric Power

Yanjun Dong, Shigeng Wang, Xiaoyu Yin, Xi Chen, Jiao Peng

2023

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 reference for the research of new power detection methods.

Paper Citation

in Harvard Style

Dong Y., Wang S., Yin X., Chen X. and Peng J. (2023). The Exploration of Small Sample-Oriented Object Detection Technology in the Field of Electric Power. In Proceedings of the 2nd International Seminar on Artificial Intelligence, Networking and Information Technology - Volume 1: ANIT; ISBN 978-989-758-677-4, SciTePress, pages 220-223. DOI: 10.5220/0012278100003807

in Bibtex Style

@conference{anit23,
author={Yanjun Dong and Shigeng Wang and Xiaoyu Yin and Xi Chen and Jiao Peng},
title={The Exploration of Small Sample-Oriented Object Detection Technology in the Field of Electric Power},
booktitle={Proceedings of the 2nd International Seminar on Artificial Intelligence, Networking and Information Technology - Volume 1: ANIT},
year={2023},
pages={220-223},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012278100003807},
isbn={978-989-758-677-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Seminar on Artificial Intelligence, Networking and Information Technology - Volume 1: ANIT
TI - The Exploration of Small Sample-Oriented Object Detection Technology in the Field of Electric Power
SN - 978-989-758-677-4
AU - Dong Y.
AU - Wang S.
AU - Yin X.
AU - Chen X.
AU - Peng J.
PY - 2023
SP - 220
EP - 223
DO - 10.5220/0012278100003807
PB - SciTePress