| Autor: |
Jun Tie, Weibo Wu, Lu Zheng, Lifeng Wu, Ting Chen |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Agriculture, Vol 14, Iss 1, p 149 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2077-0472 |
| DOI: |
10.3390/agriculture14010149 |
| Popis: |
When aiming at the problems such as missed detection or misdetection of recognizing green walnuts in the natural environment directly by using target detection algorithms, a method is proposed based on improved UNet3+ for green walnut image segmentation, which incorporates the channel and spatial attention mechanism CBAM (convolutional block attention module) and cross-entropy loss function (cross-entropy loss) into the UNet3+ network structure, and introduces the five-layer CBAM in the encoder module to construct the improved UNet3+ network model. The model consists of an encoder module (down-sampling), a decoder module (up-sampling) and a full-scale skip connection module, a full-scale feature supervision module, and a classification guidance module. After utilizing data-enhanced approaches to expand the green walnut dataset, the improved UNet3+ model was trained. The experimental findings demonstrate that the improved UNet3+ network model achieves 91.82% average precision, 96.00% recall rate, and 93.70% F1 score in the green walnut segmentation task; the addition of five-layer CBAM boosts the model segmentation precision rate by 3.11 percentage points. The method can precisely and successfully segment green walnuts, which can serve as a guide and research foundation for precisely identifying and localizing green walnuts and finishing the autonomous sorting for intelligent robots. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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