Name
Title | ||
|---|---|---|
A damage detection system for inner bore of electromagnetic railgun launcher based on deep learning and computer vision |
Abstract | ||
|---|---|---|
•An automated system for railgun launcher damage detection is proposed.•A new data set of railgun inner bore damage is obtained by the designed device.•Adaptive data augmentation and focal loss are used to balance the uneven data set.•YOLOv5 and SOLOv2 are used for the detection and shape extraction of damage.•Results on the data set show the effectiveness of the proposed methods. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.eswa.2022.117351 | Expert Systems with Applications |
Keywords | DocType | Volume |
Railgun,Damage detection,Artificial neural networks,Object detection,Instance segmentation,Data augmentation | Journal | 202 |
ISSN | Citations | PageRank |
0957-4174 | 0 | 0.34 |
References | Authors | |
0 | 6 | |