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.

摘要

在本文中，我们提出了一种应用于全球小麦头检测（GWHD）数据集的对象检测方法。我们已经通过了两种主要的目标检测架构，即 Faster R-CNN 和 EfficientDet，以设计一种新颖且鲁棒的麦头检测模型。我们强调优化我们提出的最终架构的性能。此外，我们已经进行了广泛的探索性数据分析、数据清理、数据拆分，并根据我们的环境调整了最佳数据增强技术。我们使用半监督学习，精确的伪标记，来增强以前的目标检测监督模型。此外，我们在集成学习上投入了大量精力，包括测试时间增强、多尺度集成和引导聚合以实现更高的性能。最后，我们使用加权框融合作为我们的后处理技术来优化我们的麦头检测结果。我们的解决方案已提交，以解决在 GWHD 数据集上发起的研究挑战，该数据集由来自七个国家的九个研究机构领导。我们提出的方法在上述挑战中名列前 6%。