| Title |
Comparison of Region-based CNN Methods for Defects Detection on Metal Surface |
| Authors |
이민기(Minki Lee) ; 서기성(Kisung Seo) |
| DOI |
http://doi.org/10.5370/KIEE.2018.67.7.865 |
| Keywords |
Defects detection ; Metal surface ; Convolution neural network ; Faster R-CNN ; YOLOv2 |
| Abstract |
A machine vision based industrial inspection includes defects detection and classification. Fast inspection is a fundamental problem for many applications of real-time vision systems. It requires little computation time and localizing defects robustly with high accuracy. Deep learning technique have been known not to be suitable for real-time applications. Recently a couple of fast region-based CNN algorithms for object detection are introduced, such as Faster R-CNN, and YOLOv2. We apply these methods for an industrial inspection problem. Three CNN based detection algorithms, VOV based CNN, Faster R-CNN, and YOLOv2, are experimented for defect detection on metal surface. The results for inspection time and various performance indices are compared and analysed. |