Wheat Head Detection using Deep, Semi-Supervised and Ensemble Learning

Fares Fourati, Wided Mseddi, & Rabah Attia

Abstract

In this paper, we propose an object detection methodology applied to Global Wheat Head Detection (GWHD) Dataset. We have been through two major architectures of object detection which are Faster R-CNN, 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, data cleaning, data splitting and adapted best data augmentation techniques to our context. We use semi supervised learning, precisely pseudo-labeling, to boost previous supervised models of object detection. Moreover, we put much effort on ensemble learning including test time augmentation, multi-scale ensemble and bootstrap aggregating to achieve higher performance. Finally, we use weighted boxes fusion as our post processing technique to optimize our wheat head detection results. Our solution has been submitted to solve a research challenge launched on the GWHD Dataset which was led by nine research institutes from seven countries. Our proposed method was ranked within the top 6% in the above-mentioned challenge.

[PAPER]