Name
Abstract | ||
|---|---|---|
Object detection has achieved significantly progresses in recent years. Proposal-based methods have become the mainstream object detectors, achieving excellent performance on accurate recognition and localization of objects. However, region proposal generation is still a bottleneck. In this paper, to address the limitations of conventional region proposal network (RPN) that defines dense anchor boxes with different scales and aspect ratios, we propose an anchor-free proposal generator named corner region proposal network (CRPN) which is based on a pair of key-points, including top-left corner and bottom-right corner of an object bounding box. First, we respectively predict the top-left corners and bottom-right corners by two sibling convolutional layers, then we obtain a set of object proposals by grouping strategy and non-maximum suppression algorithm. Finally, we further merge CRPN and fully convolutional network (FCN) into a unified network, achieving an end-to-end object detection. Our method has been evaluated on standard PASCAL VOC and MS COCO datasets using a deep residual network. Experiment results present that the proposed method outperforms previous detectors in the term of precision. Additionally, it runs with a speed of 76 ms per image on a single GPU by using ResNet-50 as the backbone, which is faster than other detectors. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s11042-020-09503-3 | MULTIMEDIA TOOLS AND APPLICATIONS |
Keywords | DocType | Volume |
Object detection,Anchor-free,Corners,Region proposals,Fully convolutional network | Journal | 79.0 |
Issue | ISSN | Citations |
39-40 | 1380-7501 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lin Jiao | 1 | 3 | 2.44 |
| Rujing Wang | 2 | 4 | 1.47 |
| Chengjun Xie | 3 | 51 | 9.17 |