Deep Regression Versus Detection for Counting in Robotic Phenotyping
Adrian Salazar Gomez, Erchan Aptoula, Simon Parsons, Petra Bosilj
Abstract
Work in robotic phenotyping requires computer vision methods that estimate the number of fruit or grains in an image. To decide what to use, we compared three methods for counting fruit or grains, each method representative of a class of approaches from the literature. These are two methods based on density estimation and regression (single and multiple column), and one method based on object detection. We found that when the density of objects in an image is low, the approaches are comparable, but as the density increases, counting by regression becomes steadily more accurate than counting by detection. With more than a hundred objects per image, the error in the count predicted by detection-based methods is up to 5 times higher than when using regression-based ones.
BibTeX
@inproceedings{ral2021_deepregressionve,
title = {Deep Regression Versus Detection for Counting in Robotic Phenotyping},
author = {Adrian Salazar Gomez and Erchan Aptoula and Simon Parsons and Petra Bosilj},
booktitle = {RA-L 2021},
year = {2021}
}