Using Fully Convolutional Networks for Rumex Obtusifolius Segmentation, a Preliminary Report
| Autor: | Schori Damian, Anken Thomas, Seatovic Dejan |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Artificial neural network
biology Computer science business.industry 0211 other engineering and technologies Pattern recognition 02 engineering and technology Image segmentation Rumex obtusifolius biology.organism_classification Sørensen–Dice coefficient Preliminary report Robustness (computer science) Segmentation Precision agriculture Artificial intelligence business 021101 geological & geomatics engineering |
| Zdroj: | 2019 International Symposium ELMAR. |
| Popis: | Image segmentation of specific plants is an important task in precision farming. Several influences such as changing light, varying arrangement of leaves and similarly looking plants are challenging. We present a solution for segmenting individual Rumex obtusifolius plants out of complicated natural scenes in grassland from 2D images. We are making use of a fully convolutional deep neural network (FCN) trained with hand labeled images. The proposed segmentation scheme is validated with images taken under outdoor conditions. The overall masks segmentation rate is 84.8% measured by the dice coefficient. Approximately half of the experiments show segmentation rates of individual plants higher than 88%. The developed solution is therefore a robust method to segment Rumex obtusifolius plants under real-world conditions in short time. |
| Databáze: | OpenAIRE |
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