Wheat Head Detection

Wheat Head Detection

This page is resource for AICrowd: Global Wheat Challenge 2021

Global Wheat Head Detection (GWHD) Dataset: A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods

Previous Competition Solution

Kaggle Global Wheat Detection 2020

1st place solution

https://www.kaggle.com/c/global-wheat-detection/discussion/172418

Summary

  • Custom mosaic data augmentation
  • MixUp
  • Heavy augmentation
  • Data cleaning
  • EfficientDet
  • Faster RCNN FPN
  • Ensemble multi-scale model: Weighted-Boxes-Fusion, special thank @zfturbo
  • Test time augmentation(HorizontalFlip, VerticalFlip, Rotate90)
  • Pseudo labeling

2nd Place Solution

Link: https://www.kaggle.com/c/global-wheat-detection/discussion/175961

Repository: https://github.com/liaopeiyuan/TransferDet

3rd Place Solution

Code repository https://github.com/ufownl/global-wheat-detection

  • YOLOv3 from GluonCV
  • Use Darknet53 backbone
  • Use WBF over TTA
  • Use pseudo-labeling technique

Papers & Literature

Code

Yolo5 was not eligible for 2020 Wheat Head Challenge, but it can be used in 2021 Wheat Head Challenge [discussion]

YoloV5 vs EfficientDet

Articles

Wheat Dataset

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