Wheat Head Detection

Wheat Head Detection
This page is resource for AICrowd: Global Wheat Challenge 2021

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
Papers & Literature
- EfficientDet: Scalable and Efficient Object Detection
- An analysis of last year competition: [2105.06182] Global Wheat Challenge 2020: Analysis of the competition design and winning models (arxiv.org)
- An introduction on the new datasets : [2105.07660] Global Wheat Head Dataset 2021: an update to improve the benchmarking wheat head localization with more diversity (arxiv.org)
- Roman Solovyev, Wimin Wang, Tatiana Gbruseva, Weighted boxes fusion: Ensembling boxes from different object detection models with [Code]
- 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 https://hal.archives-ouvertes.fr/hal-02560389
- Wheat Head Detection using Deep, Semi-Supervised and Ensemble Learning https://www.tandfonline.com/doi/abs/10.1080/07038992.2021.1906213?journalCode=ujrs20
- Bo Gong, Daji Ergu, Ying Cai, Bo Ma, Real-Time Detection for Wheat Head Applying Deep Neural Network https://pubmed.ncbi.nlm.nih.gov/33396711/
- Saeed Khaki, Nima Safaei, Hieu Pham, Lizhi Wang, WheatNet: A Lightweight Convolutional Neural Network for High-throughput Image-based Wheat Head Detection and Counting https://arxiv.org/abs/2103.09408
- Md Mehedi Hasan, Joshua P. Chopin, Hamid Laga & Stanley J. Miklavcic , Detection and analysis of wheat spikes using Convolutional Neural Networks https://plantmethods.biomedcentral.com/articles/10.1186/s13007-018-0366-8
- Do Thuan, EVOLUTION OF YOLO ALGORITHM AND YOLOV5: THE STATE-OF-THE-ART OBJECT DETECTION ALGORITHM , https://www.theseus.fi/bitstream/handle/10024/452552/Do_Thuan.pdf
Code
- EfficientDet (Scalable and Efficient Object Detection) implementation in Keras and Tensorflow
- Signatrix/efficientdet: Pytorch implementation
- xuannianz/EfficienDet: Implementation on Keras & Tensorflow
- Google: EfficientDet
- rwightman/efficientdet : A PyTorch implementation of EfficientDet. (used in 1st & 2nd place Kaggle 2020)
- YoloV5 https://github.com/ultralytics/yolov5
- Global Wheat Competition 2021, Starting Notebook using the WILDS library
Yolo5 was not eligible for 2020 Wheat Head Challenge, but it can be used in 2021 Wheat Head Challenge [discussion]

Articles
- EfficientDet: Towards Scalable and Efficient Object Detection
- A Throrough Breakdown of EfficientDet for Object Detection