dataset_info:
features:
- name: id
dtype: int8
- name: original_name
dtype: string
- name: image
dtype: image
- name: det_annotation
sequence:
- name: bbox
sequence: float32
length: 4
- name: category
dtype:
class_label:
names:
'0': precipitate
- name: seg_annotation
dtype: image
- name: raw_seg_annotation
dtype: string
splits:
- name: train
num_bytes: 7130619
num_examples: 15
- name: validation
num_bytes: 2195097
num_examples: 4
- name: test
num_bytes: 1956008
num_examples: 5
download_size: 10468587
dataset_size: 11281724
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
DT_SegNet Dataset
About The Project
The performance of advanced materials for extreme environments is underpinned by their microstruc- ture, such as the size and distribution of nano- to micro-sized reinforcing phase(s). Chromium-based superalloys are a recently proposed alternative to conventional face-centred-cubic superalloys for high-temperature applications, e.g., Concentrated Solar Power. Their development requires the de- termination of precipitate volume fraction and size distribution using Electron Microscopy (EM), as these properties are crucial for the thermal stability and mechanical properties of chromium superal- loys. Traditional approaches to EM image processing utilise filtering with a fixed contrast threshold, leading to weak robustness to background noise and poor generalisability to different materials. It also requires an enormous amount of time for manual object measurements. Efficient and accurate object detection and segmentation are therefore highly desired to accelerate the development of novel materials like chromium-based superalloys. To address these bottlenecks, based on YOLOv5 and SegFormer structures, this study proposes an end-to-end, two-stage deep learning scheme, DT- SegNet, to perform object detection and segmentation for EM images. The proposed approach can thus benefit from the training efficiency of Convolutional Neural Networks at the detection stage (i.e., a small number of training images required) and the accuracy of the Vision Transformer at the segmentation stage. Extensive numerical experiments demonstrate that the proposed DT-SegNet significantly outperforms the state-of-the-art segmentation tools offered by Weka and ilastik regard- ing a large number of metrics, including accuracy, precision, recall and F1-score. This model will be a meaningful tool for accelerating alloy development and microstructure examination.
Dataset
All data for this project are stored in the data/ folder.
Apache Parquet is used for a more efficient storage format.
The dataset is split into three sets: test, train, and validation.
Detection annotation format follows the YOLO format, and segmentation annotation is stored as a PNG image.
The category label is 0 for precipitate.
Reference
@article{xia2023Accurate,
author = {Zeyu Xia and Kan Ma and Sibo Cheng and Thomas Blackburn and Ziling Peng and Kewei Zhu and Weihang Zhang and Dunhui Xiao and Alexander J Knowles and Rossella Arcucci},
copyright = {CC BY-NC 3.0},
doi = {10.1039/d3cp00402c},
issn = {1463-9076},
journal = {Physical Chemistry Chemical Physics},
keywords = {},
language = {English},
month = {6},
number = {23},
pages = {15970--15987},
pmid = {37265373},
publisher = {Royal Society of Chemistry (RSC)},
title = {Accurate Identification and Measurement of the Precipitate Area by Two-Stage Deep Neural Networks in Novel Chromium-Based Alloy},
url = {https://pubs.rsc.org/en/content/articlelanding/2023/CP/D3CP00402C},
volume = {25},
year = {2023}
}
Contact
Zeyu Xia - [email protected]
Kan Ma - [email protected]
Sibo Cheng - [email protected]