  In this paper, we propose an original object detection methodology applied to
Global Wheat Head Detection (GWHD) Dataset. We have been through two major
architectures of object detection which are FasterRCNN and EfficientDet, in
order to design a novel and robust wheat head detection model. We emphasize on
optimizing the performance of our proposed final architectures. Furthermore, we
have been through an extensive exploratory data analysis and adapted best data
augmentation techniques to our context. We use semi supervised learning to
boost previous supervised models of object detection. Moreover, we put much
effort on ensemble to achieve higher performance. Finally we use specific
post-processing techniques to optimize our wheat head detection results. Our
results have been submitted to solve a research challenge launched on the GWHD
Dataset which is led by nine research institutes from seven countries. Our
proposed method was ranked within the top 6% in the above mentioned challenge.
