TITLE: Global Wheat Head Detection (GWHD) dataset: a large and diverse dataset of high resolution RGB labelled images to develop and benchmark wheat head detection methods 

ABSTRACT: Detection of wheat heads is an important task allowing to estimate pertinent
traits including head population density and head characteristics such as
sanitary state, size, maturity stage and the presence of awns. Several studies
developed methods for wheat head detection from high-resolution RGB imagery.
They are based on computer vision and machine learning and are generally
calibrated and validated on limited datasets. However, variability in
observational conditions, genotypic differences, development stages, head
orientation represents a challenge in computer vision. Further, possible
blurring due to motion or wind and overlap between heads for dense populations
make this task even more complex. Through a joint international collaborative
effort, we have built a large, diverse and well-labelled dataset, the Global
Wheat Head detection (GWHD) dataset. It contains 4,700 high-resolution RGB
images and 190,000 labelled wheat heads collected from several countries around
the world at different growth stages with a wide range of genotypes. Guidelines
for image acquisition, associating minimum metadata to respect FAIR principles
and consistent head labelling methods are proposed when developing new head
detection datasets. The GWHD is publicly available at
http://www.global-wheat.com/ and aimed at developing and benchmarking methods
for wheat head detection.
Detection of wheat heads is an important task allowing to estimate pertinent
traits including head population density and head characteristics such as
sanitary state, size, maturity stage and the presence of awns. Several studies
developed methods for wheat head detection from high-resolution RGB imagery.
They are based on computer vision and machine learning and are generally
calibrated and validated on limited datasets. However, variability in
observational conditions, genotypic differences, development stages, head
orientation represents a challenge in computer vision. Further, possible
blurring due to motion or wind and overlap between heads for dense populations
make this task even more complex. Through a joint international collaborative
effort, we have built a large, diverse and well-labelled dataset, the Global
Wheat Head detection (GWHD) dataset. It contains 4,700 high-resolution RGB
images and 190,000 labelled wheat heads collected from several countries around
the world at different growth stages with a wide range of genotypes. Guidelines
for image acquisition, associating minimum metadata to respect FAIR principles
and consistent head labelling methods are proposed when developing new head
detection datasets. The GWHD is publicly available at
http://www.global-wheat.com/ and aimed at developing and benchmarking methods
for wheat head detection.
Global Wheat Head Detection (GWHD) dataset: a large and diverse dataset of high resolution RGB labelled images to develop and benchmark wheat head detection methods
http://arxiv.org/abs/2005.02162v2