Name
Abstract | ||
|---|---|---|
Object detection is a crucial topic in computer vision. Mask Region-Convolution Neural Network (R-CNN) based methods, wherein a large intersection over union (IoU) threshold is chosen for high quality samples, have often been employed for object detection. However, the detection performance of such methods deteriorates when samples are reduced. To address this, the authors propose an improved Mask... |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1049/iet-ipr.2019.0057 | IET Image Processing |
Keywords | DocType | Volume |
computer vision,feature extraction,image classification,image coding,image representation,learning (artificial intelligence),neural nets,object detection | Journal | 14 |
Issue | ISSN | Citations |
8 | 1751-9659 | 4 |
PageRank | References | Authors |
0.42 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Minghu Wu | 1 | 60 | 2.81 |
| hanhui yue | 2 | 4 | 0.42 |
| Juan Wang | 3 | 109 | 27.00 |
| Yongxi Huang | 4 | 4 | 0.42 |
| Min Liu | 5 | 56 | 16.44 |
| Yuhang Jiang | 6 | 4 | 0.42 |
| Cong Ke | 7 | 4 | 0.42 |
| Cheng Zeng | 8 | 4 | 0.42 |