| Title |
Embedded Deep Learning System for Defects Detection |
| Authors |
이건영(Keon Young Yi) ; 정선재(Sunjae Jeong) ; 서기성(Kisung Seo) |
| DOI |
https://doi.org/10.5370/KIEE.2020.69.2.325 |
| Keywords |
Defects detection; Convolutional neural network; Network Reduction; Embedded System; YOLOv2; YOLOv3; YOLOv2-tiny |
| Abstract |
A machine vision based industrial inspection requires little computation time and localizing defects robustly with high accuracy.
Recent mobile and embedded systems require computationally efficient machine intelligence with a deep learning model. In order to improve detection performance and processing time, various network modification methods are proposed. The experiments for defect detection on the metal surfaces data are executed using the various YOLO networks on embedded GPU system Nvidia Tx-1. The results for detection performance and inspection time are compared and analysed. Among them, modified YOLOv2-tiny model shows a better performance in both detection rate and fps |