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

An original framework for Wheat Head Detection using Deep, Semi-supervised and Ensemble Learning within Global Wheat Head Detection (GWHD) Dataset