TY - GEN
T1 - Phenotyping Problems of Parts-per-Object Count
AU - Khoroshevsky, Faina
AU - Khoroshevsky, Stanislav
AU - Markovich, Oshry
AU - Granitz, Orit
AU - Bar-Hillel, Aharon
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - The need to count the number of parts per object arises in many yield estimation problems, like counting the number of bananas in a bunch, or the number of spikelets in a wheat spike. We propose a two-stage detection and counting approach for such tasks, operating in field conditions with multiple objects per image. The approach is implemented as a single network, tested on the two mentioned problems. Experiments were conducted to find the optimal counting architecture and the most suitable training configuration. In both problems, the approach showed promising results, achieving a mean relative deviation in range of 11 % – 12 % of the total visible count. For wheat, the method was tested in estimating the average count in an image, and was shown to be preferable to a simpler alternative. For bananas, estimation of the actual physical bunch count was tested, yielding mean relative deviation of 12.4 %.
AB - The need to count the number of parts per object arises in many yield estimation problems, like counting the number of bananas in a bunch, or the number of spikelets in a wheat spike. We propose a two-stage detection and counting approach for such tasks, operating in field conditions with multiple objects per image. The approach is implemented as a single network, tested on the two mentioned problems. Experiments were conducted to find the optimal counting architecture and the most suitable training configuration. In both problems, the approach showed promising results, achieving a mean relative deviation in range of 11 % – 12 % of the total visible count. For wheat, the method was tested in estimating the average count in an image, and was shown to be preferable to a simpler alternative. For bananas, estimation of the actual physical bunch count was tested, yielding mean relative deviation of 12.4 %.
KW - Deep neural networks
KW - Object detection
KW - Part counting
KW - Yield estimation
UR - https://www.scopus.com/pages/publications/85101386511
U2 - 10.1007/978-3-030-65414-6_19
DO - 10.1007/978-3-030-65414-6_19
M3 - Conference contribution
AN - SCOPUS:85101386511
SN - 9783030654139
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 261
EP - 278
BT - Computer Vision – ECCV 2020 Workshops, Proceedings
A2 - Bartoli, Adrien
A2 - Fusiello, Andrea
PB - Springer Science and Business Media Deutschland GmbH
T2 - Workshops held at the 16th European Conference on Computer Vision, ECCV 2020
Y2 - 23 August 2020 through 28 August 2020
ER -