Name
Title | ||
|---|---|---|
Pomelo Tree Detection Method Based on Attention Mechanism and Cross-Layer Feature Fusion |
Abstract | ||
|---|---|---|
Deep learning is the subject of increasing research for fruit tree detection. Previously developed deep-learning-based models are either too large to perform real-time tasks or too small to extract good enough features. Moreover, there has been scarce research on the detection of pomelo trees. This paper proposes a pomelo tree-detection method that introduces the attention mechanism and a Ghost module into the lightweight model network, as well as a feature-fusion module to improve the feature-extraction ability and reduce computation. The proposed method was experimentally validated and showed better detection performance and fewer parameters than some state-of-the-art target-detection algorithms. The results indicate that our method is more suitable for pomelo tree detection. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.3390/rs14163902 | REMOTE SENSING |
Keywords | DocType | Volume |
convolutional neural network, object detection, attention mechanism, remote-sensing image, pomelo tree detection | Journal | 14 |
Issue | ISSN | Citations |
16 | 2072-4292 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haotian Yuan | 1 | 0 | 0.34 |
| Kekun Huang | 2 | 0 | 0.68 |
| Chuan-Xian Ren | 3 | 259 | 22.50 |
| Yongzhu Xiong | 4 | 0 | 0.34 |
| Jieli Duan | 5 | 0 | 0.34 |
| Zhou Yang | 6 | 1 | 5.70 |