| Title |
Application of Lightweight CNN Model for Detecting Defects of Substation Insulators using Drone EO/IR Camera |
| Authors |
유지환(Jihwan You) ; 나원상(Won-Sang Ra) ; 김영근(Young-Keun Kim) |
| DOI |
https://doi.org/10.5370/KIEE.2022.71.3.540 |
| Keywords |
Defect Insulator; Light-weight deep learning; Real-Time Object Detection; Drone Surveillance; EO/IR Image; Embedded GPU |
| Abstract |
This paper proposes a monitoring system of defected substation insulators based on drone EO/IR images and a light-weight deep learning model. The defected substation insulators generate corona discharge and excessive heating. Thus, this study used RGB-thermal blended images from drone EO/IR cameras to monitor insulator failures, which is not visible to naked eyes. Also, this paper compared several light-weighted object detection models to select the most suitable model to deploy on the embedded processor of drones. The applied compressed CNN model, YOLOv4 backbone with group convolution and channel shuffle operations, is sufficiently fast and light-weighted for an embedded GPU processor. Experiments with mockup insulators with corona discharge showed that the proposed system resulted 99.50% mAP, 5.9fps on embedded GPU(Jetson Nano), and has 3.9MB memory that is 37.99 times lighter than YOLOv4. |