Name
Title | ||
|---|---|---|
Real-Time Metal-Surface-Defect Detection and Classification Using Advanced Machine Learning Technique |
Abstract | ||
|---|---|---|
In this paper, an advanced machine learning technique is proposed to enable robust real-time metal-surface-detect detection and classification using video streams. The industrial informatics can be inferred from video data according to our proposed new approach. Different from the conventional schemes, our proposed machine-learning technique can detect and classify the metal-surface defects by selecting critical statistical and structural features using Renyi's entropy. To demonstrate the effectiveness of our proposed new detection and classification algorithm, simulation results and performances are compared with the prevalent conventional decision-tree classifier. Based on numerous experimental results, our proposed metal-surface defect detection and classification scheme greatly outperforms the conventional decision-tree classifier. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/BMSB55706.2022.9828748 | 2022 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB) |
Keywords | DocType | ISSN |
Metal-surface defect detection and classification,video data,Renyi's entropy,decision tree,feature selection | Conference | 2155-5044 |
ISBN | Citations | PageRank |
978-1-6654-6902-9 | 0 | 0.34 |
References | Authors | |
8 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Liu | 1 | 11 | 21.81 |
| Kun Yan | 2 | 0 | 0.68 |
| Hsiao-chun Wu | 3 | 959 | 97.99 |
| Xiangli Zhang | 4 | 0 | 0.34 |
| Shih Yu Chang | 5 | 0 | 0.34 |
| Wu, Yiyan | 6 | 13 | 7.47 |