A Lightweight Electric Meter Recognition Model for Power Inspection Robots

Shuangshuang Song, Hongsai Tian and Feng Zhao ()
Additional contact information
Shuangshuang Song: School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan 250357, China
Hongsai Tian: School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan 250357, China
Feng Zhao: School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan 250357, China

Energies, 2024, vol. 17, issue 18, 1-18

Abstract: Power inspection robots are essential for ensuring safe and optimal operation of power systems. However, during the operation of the power inspection robot, constraints imposed by computational and storage resources slow down the detection speed of the power system, failing to meet real-time monitoring requirements. To address these issues, this study proposes a lightweight electric meter recognition model for power inspection robots based on YOLOv5. The aim is to ensure efficient operation of the model on embedded devices, achieve real-time meter recognition, and enhance the practicality of the inspection robot. In the proposed model, GhostNet, a lightweight network, is employed as the YOLOv5 backbone feature extraction module, thus improving the model’s computational efficiency. In addition, the Wise-IoU (WIoU) loss function is used to improve the localization accuracy of the electric meter recognition model. Moreover, the GSConv module was introduced in the neck network for further model lightweighting. The experimental results demonstrated that the proposed model achieves a recognition accuracy of 98.8%, a recall rate of 98.8%, and a frame rate of 416.67 frames per second, while reducing computational volume by 25% compared to the YOLOv5 model. Furthermore, through case studies and comparisons, we illustrated the effectiveness and superiority of the proposed approach.

Keywords: power inspection robots; deep learning; electric meter recognition; GhostNet; loss function (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: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/18/4731/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/18/4731/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:18:p:4731-:d:1483310

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().