Name
Abstract | ||
|---|---|---|
•Ensemble learning improves the performance of object detection and achieves the mAP of state-of-the-art detectors.•The combination of context modeling and dilated convolution ensures the detection speed.•The proposed multi-scale feature fusion module confers a clear improvement to the detector.•The proposed ensemble modes demonstrate the effectiveness of ensemble learning in the field of object detection. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.patcog.2019.107098 | Pattern Recognition |
Keywords | Field | DocType |
Ensemble learning,Object detection,Dilated convolution,Feature fusion | Spatial analysis,Object detection,Pattern recognition,Convolution,Curse of dimensionality,Context model,Artificial intelligence,Deep learning,Detector,Ensemble learning,Mathematics | Journal |
Volume | Issue | ISSN |
99 | 1 | 0031-3203 |
Citations | PageRank | References |
4 | 0.54 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jie Xu | 1 | 6 | 1.95 |
| Wei Wang | 2 | 338 | 32.88 |
| Hanyuan Wang | 3 | 4 | 0.54 |
| Jinhong Guo | 4 | 10 | 5.03 |