论文标题
全球小麦头部检测（GWHD）数据集中使用深，半监督和合奏学习的小麦头部检测的原始框架
An original framework for Wheat Head Detection using Deep, Semi-supervised and Ensemble Learning within Global Wheat Head Detection (GWHD) Dataset
论文作者
论文摘要
在本文中，我们提出了一种适用于全球小麦头部检测（GWHD）数据集的原始对象检测方法。为了设计一种新颖且健壮的小麦头部检测模型，我们经过了两个主要的对象检测的主要架构，它们是更简单的和有效的。我们强调优化提议的最终体系结构的性能。此外，我们已经通过广泛的探索性数据分析，并将最佳数据增强技术适应了我们的背景。我们使用半监督的学习来促进先前的对象检测模型。此外，我们为实现更高的性能而付出了很多努力。最后，我们使用特定的后处理技术来优化我们的小麦头部检测结果。我们的结果已提交，以解决在GWHD数据集中发起的研究挑战，该数据集由来自七个国家的九个研究机构领导。在上述挑战中，我们提出的方法排在最高的6％之内。
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.
