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Birds Detection in Natural Scenes Based on Improved Faster RCNN
| Content Provider | MDPI |
|---|---|
| Author | Xiang, Wenbin Song, Ziying Zhang, Guoxin Wu, Xuncheng |
| Copyright Year | 2022 |
| Description | To realize the accurate detection of small-scale birds in natural scenes, this paper proposes an improved Faster RCNN model to detect bird species. Firstly, the model uses a depth residual network to extract convolution features and performs multi-scale fusion for feature maps of different convolutional layers. Secondly, the K-means clustering algorithm is used to cluster the bounding boxes. We improve the anchoring according to the clustering results. The improved anchor frame tends toward the real bounding box of the dataset. Finally, the Soft Non-Maximum Suppression method is used to reduce the missed detection of overlapping birds. Compared with the original model, the improved model has faster effect and higher accuracy. |
| Starting Page | 6094 |
| e-ISSN | 20763417 |
| DOI | 10.3390/app12126094 |
| Journal | Applied Sciences |
| Issue Number | 12 |
| Volume Number | 12 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-06-15 |
| Access Restriction | Open |
| Subject Keyword | Applied Sciences Industrial Engineering Deep Residual Network Faster Rcnn Model Multi-scale Fusion Soft Non-maximum Suppression |
| Content Type | Text |
| Resource Type | Article |
