Ammeter Inspection With Densely Connected Object Detection Network
- Resource Type
- Conference
- Authors
- Yan, Hong; Jiao, Zhiqiang; Yang, Muwei; Wang, Qiang; Yang, Ning; Ma, Lijun
- Source
- 2019 6th International Conference on Systems and Informatics (ICSAI) Systems and Informatics (ICSAI), 2019 6th International Conference on. :345-349 Nov, 2019
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
General Topics for Engineers
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Erbium
Conferences
Informatics
Object detection
Deep learning
Electric metering device
object detection
YOLO
DenseNet
- Language
It is very important to understand the safety status of the electric metering devices in our daily life. This paper proposes a method based on deep learning to detect and evaluate electric metering devices. The detection Convolutional Neural Network (CNN) model is designed with the end-to-end architecture of You Only Look Once version 3(YOLOv3), but the backbone network is substituted with Densnet201 based on the dense connection idea. The detection objects include ammeter components of various sizes. The experimental results show that the proposed method can effectively detect the electric metering device in real time.