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Cucumber Detection and Segmentation
Asher Yartsev Or Shemesh Simon lousky
Supervised by Alon Zvirin
This Project tackles the task of segmentation and indexation on cucumber plant parts. The approach is to implement an end to end workflow to partially annotated and relatively small datasets.
Mask RCNN paper from facebook research has proven very efficient in this task even on small datasets. Therefore the focus is maximizing the effectiveness of a problematic dataset on different levels. This reports discusses partially annotated datasets, and inconsistent annotation styles.
The conclusion tends towards a trade-off between investing in the dataset quality and using augmentation acrobatics to quickly use the existing data. There is no doubt that consistent fully annotated real pictures will get the most out of the network.
Please, see project papers.
Please, see project report.