Automated Pollen Detection with an Affordable Technology

This repository contains the code and hardware design files accompanying the paper Automated and Continuous Pollen Detection with an Affordable Technology. The data can be found here (download instructions provided below).

Bibtex citation

@inproceedings{namcao2020pollen,
  title = {Automated Pollen Detection with an Affordable Technology},
  author = {Nam Cao and Matthias Meyer and Lothar Thiele and Olga Saukh},
  booktitle = {Proceedings of the International Conference on Embedded Wireless Systems and Networks (EWSN)},
  pages={108–119}
  month = {2},	
  year = {2020},
}

Hardware designs

The ./hwdesign/ folder contains the design files for the automated pollen trap. For more information please refer to the paper.

Code

The paper's results can be reproduced with the following guideline.

Requirements

To use the code, you require the following tools as prerequisites:

• Python 3.7
• git

We recommend Anaconda or Miniconda, the latter being a minimal (but sufficient) version of the Anaconda distribution. The following instructions will be based on Miniconda. If you use another Python environment, the installation routine must be adapted.

Note: The code was only tested on Linux. Using Windows might lead to problems during installation of packages. If you get it to run on Windows please create a pull request with instructions/required changes.

Quickstart

After the installation of Anaconda, open a terminal (on Windows Anaconda Prompt) and create a new environment by typing:

Clone repository and install requirements

git clone https://github.com/osaukh/pollenpub
cd code/
conda env create -f environment.yml

Note: The following commands are to be run from the code/ directory.

Download weights and pollen images

python utils/download.py -f weights.zip data.zip

Run the test

python test.py --weights_path ../weights/pollen_20190526.pth --model_def config/yolov3-pollen.cfg --data_config config/test_20190523.data

Run object detection on any image

Change the --image_folder argument to an image directory which contains pollen images and detect pollen images with the following command:

python detect.py --image_folder ../data/pollen_20190523/layers/ --output_folder ../tmp/output/ 

Training

Training on the provided dataset can be done by issuing the following command

python train.py --name fold0 --epochs=60 --model_def config/yolov3-pollen.cfg --data_config config/train_20190526fold0.data --pretrained_weights ../weights/darknet53.conv.74

Prepare new pollen training sets

The create_folds.py script can be used to prepare the image folders to be used with the training script. It creates one/multiple text files which can be used for training/testing. Each file contains the name of the images belonging to the train/val set. The script takes into account that each sample consists of multiple depth layers since it is important that all depth layers of a sample are either in train or val set. This avoids information leakage between test and val,

Note: The following commands need only to be exectuted if you want to use a new labeled image folder for training. The files produced by these commands are already in ./config/.

After preparing the data you can run the training procedure as described before but with updated --data_config parameter.

Create training an validation set

python create_folds.py -f ../data/pollen_20190526/ -o ../data/pollen_20190526/ -K 5 -n train_20190526

Create test set

python create_folds.py -f ../data/pollen_20190523/ -o ../data/pollen_20190523/ -n test_20190523

Credit

PyTorch-YOLOv3 repository

Thanks to Erik Linder-Norén who open sourced his YOLOv3 code on which this implementation is based.

YOLOv3: An Incremental Improvement

Joseph Redmon, Ali Farhadi

[Paper] [Project Webpage] [Authors' Implementation]

@article{yolov3,
  title={YOLOv3: An Incremental Improvement},
  author={Redmon, Joseph and Farhadi, Ali},
  journal = {arXiv},
  year={2018}
}