Name
Title | ||
|---|---|---|
CADN: A weakly supervised learning-based category-aware object detection network for surface defect detection |
Abstract | ||
|---|---|---|
•A novel Category-aware Conv-Pooling module is proposed, which explores weak image tag annotation to extract spatial information.•Knowledge distillation strategy is adopted to force the feature of a student CADN to mimic that of a teacher CADN, leading to accuracy improvement.•As verified, weakly supervised defect detection is achieved and competitive results are obtained by using the proposed CADN method.•In CADN, human labeling effort, accuracy and speed are simultaneously considered, making the method practical in industrial applications. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.patcog.2020.107571 | Pattern Recognition |
Keywords | DocType | Volume |
Weakly supervised learning,Automated surface inspection,Defect detection,Knowledge distillation | Journal | 109 |
Issue | ISSN | Citations |
1 | 0031-3203 | 3 |
PageRank | References | Authors |
0.39 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiabin Zhang | 1 | 5 | 1.43 |
| Hu Su | 2 | 4 | 1.75 |
| Wei Zou | 3 | 18 | 3.05 |
| Xin-Yi Gong | 4 | 4 | 2.09 |
| Zheng-Tao Zhang | 5 | 57 | 8.00 |
| Fei Shen | 6 | 20 | 3.28 |