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This paper describes a fruit counting pipeline based on deep learning that accurately counts fruit in unstructured environments.Finally, a linear regression model maps that fruit count estimate to a final fruit count.A blob detector based on a fully convolutional network extracts candidate regions in the images.
This paper describes a fruit counting pipeline based
on deep learning that accurately counts fruit in unstructured
environments. Obtaining reliable fruit counts is challenging
because of variations in appearance due to illumination changes
and occlusions from foliage and neighboring fruits. We propose
a novel approach that uses deep learning to map from input
images to total fruit counts. The pipeline utilizes a custom
crowd-sourcing platform to quickly label large data sets. A
blob detector based on a fully convolutional network extracts
candidate regions in the images. A counting algorithm based
on a second convolutional network then estimates the number
of fruit in each region. Finally, a linear regression model maps
that fruit count estimate to a final fruit count. We analyze the
performance of the pipeline on two distinct data sets of oranges in
daylight, and green apples at night, utilizing human generated
labels as ground truth. We also show that the pipeline has a
short training time and performs well with a limited data set
size. Our method generalizes across both data sets and is able to
perform well even on highly occluded fruits that are challenging
for human labelers to annotate.
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