This repository contains detailed information for our ECAI2023 paper entitled Identifying the Defective: Detecting Damaged Grains for Cereal Appearance Inspection (ECAI2023: https://ebooks.iospress.nl/doi/10.3233/FAIA230329 , ArXiv: https://arxiv.org/abs/2311.11901).

• Python3.8

• Packages or pip install -r requirements.txt

  torch==1.12.1
  torchvision==0.13.1
  numpy==1.22.4
  opencv-python==4.5.1

To train on the OOD-GrainSet (see below) and extract it.

The dataset folders/files follow its original structure.

To train on the MVtec Anomaly Detection dataset download the data and extract it. The Describable Textures dataset was used as the anomaly source image set in most of the experiments in the paper. You can run the download_dataset.sh script from the project directory to download the DTD datasets to the texture folder in the project directory:

./texture/download_dataset.sh

The dataset folders/files follow its original structure.

Please specicy dataset path (line1) and log folder (line10) in run_*.sh before running. Edit run_*.sh to edit dataset class and dataset path.(such as run_mvtec.sh,run_wheat_set1.sh,...)

run_*.sh gives the configuration to train models on MVTecAD dataset.

bash run_*.sh

Our dataset, called OOD-GrainSet, involves two types of cereal grains: wheat and maize.

• For wheat data, we annotated about $180K$ single-kernel images, including $145K$ healthy grains and $5K$ images for each damaged grain category and impurities.
• For maize data, we annotated about $40K$ single-kernel images, including $33K$ healthy grains and $1K$ images for each damaged grain category and impurities.

Based on the whole OOD-GrainSet, we conduct two data partition schedules in experiments, i.e.,

• Set1: healthy grains vs. damaged grains
• Set2: edible grains vs. inedible grains (BN, AP, BP and HD are also edible to some extent)

• Wheat:
  • Images: OOD-GrainSet-Wheat (180k, 17.6GB)
  • Training/test file index OOD-GrainSet-Wheat train/test.txt and category information (5 files, 42MB)
• Maize
  • Images OOD-GrainSet-Maize (40K, 13.4GB)
  • Training/test file index OOD-GrainSet-Maize train/test.txt and category information (5 files, 9MB)

	HY	SD	FS	BP/HD	MY	BN	AP	IM
Wheat
Maize

Overview

• Raw grain smaples to Image data

• Sample Collection

  • Crops are havested and transported into the grain centers
  • Raw grains are sampled to extract laboratory samples
  • Laboratory samples are further pre-processed by using handed tools, such as sieves, fans, tweezers

• Image Acquisition

  • Grain kernels are fed into our device
  • Our framework can produce a number of single-kernel images

• Image Annotation

• we have engaged a team of six experienced annotators:
  • 1. four senior inspectors with 5-10 years of inspection experience and currently working in authoritative quality inspection centers;
  • 2. two experts with over 10 years of experience and holding leadership positions in the quality inspection center, who are also involved in the development of several national standards related to cereal grains.
• The annotation for each kernel involves multiple steps to ensure consistency and accuracy:
  • 1. if all 4 senior inspectors assign the same label, then the label is confirmed (more than 99.9% of kernels are annotated in this step);
  • 2. in case where 3 of 4 senior inspectors assign the same label, then two experts are consulted to reach a final agreement and confirm the label;
  • 3. the remaining images are discarded.
• To maintain the high quality of our dataset, we exclusively include samples from step1 and step2 samples in our OOD-GrainSet.

If our paper has been of assistance, we would appreciate it if you could consider citing it in your work.

@incollection{fan2023ai4graininsp,
  title={Identifying the Defective: Detecting Damaged Grains for Cereal Appearance Inspection},
  author={Fan, Lei and Ding, Yiwen and Fan, Dongdong and Wu, Yong and Pagnucco, Maurice and Song, Yang},
  booktitle={ECAI 2023},
  year={2023},
  publisher={IOS Press}
}



@inproceedings{fan2022grainspace,
  title={GrainSpace: A Large-scale Dataset for Fine-grained and Domain-adaptive Recognition of Cereal Grains},
  author={Fan, Lei and Ding, Yiwen and Fan, Dongdong and Di, Donglin and Pagnucco, Maurice and Song, Yang},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={21116--21125},
  year={2022}
}


@article{fan2023annotated,
  title={An annotated grain kernel image database for visual quality inspection},
  author={Fan, Lei and Ding, Yiwen and Fan, Dongdong and Wu, Yong and Chu, Hongxia and Pagnucco, Maurice and Song, Yang},
  journal={Scientific Data},
  volume={10},
  number={1},
  pages={778},
  year={2023},
  publisher={Nature Publishing Group UK London}
}



@article{fan2023av4gainsp,
  title={AV4GAInsp: An Efficient Dual-Camera System for Identifying Defective Kernels of Cereal Grains},
  author={Fan, Lei and Ding, Yiwen and Fan, Dongdong and Wu, Yong and Chu, Hongxia and Pagnucco, Maurice and Song, Yang},
  journal={IEEE Robotics and Automation Letters},
  year={2023},
  publisher={IEEE}
}

Thanks for SimpleNet.

All code within the repo is under MIT license