Dataset: Industrial Machine Tool Element Surface Defect Dataset

Using Machine Learning Techniques in general and Deep Learning techniques in specific needs a certain amount of data often not available in large quantities in some technical domains. The manual inspection of Machine Tool Components, as well as the manual end of line check of products, are labour intensive tasks in industrial applications that often want to be automated by companies. To automate the classification processes and to develop reliable and robust Machine Learning based classification and wear prognostics models there is a need for real-world datasets to train and test models on.

The dataset contains 1104 channel 3 images with 394 image-annotations for the surface damage type “pitting”. The annotations made with the annotation tool labelme, are available in JSON format and hence convertible to VOC and COCO format. All images come from two BSD types. The dataset available for download is divided into two folders, data with all images as JPEG, label with all annotations, and saved_model with a baseline model. The authors also provide a python script to divide the data and labels into three different split types – train_test_split, which splits images into the same train and test data-split the authors used for the baseline model, wear_dev_split, which creates all 27 wear developments and type_split, which splits the data into the occurring BSD-types.
One of the two mentioned BSD types is represented with 69 images and 55 different image-sizes. All images with this BSD type come either in a clean or soiled condition.
The other BSD type is shown on 325 images with two image-sizes. Since all images of this type have been taken with continuous time the degree of soiling is evolving.
Also, the dataset contains as above mentioned 27 pitting development sequences with every 69 images.
Instruction dataset split
The authors of this dataset provide 3 types of different dataset splits.
To get the data split you have to run the python script split_dataset.py.
Script inputs:
split-type (mandatory)
output directory (mandatory)
Different split-types:
train_test_split: splits dataset into train and test data (80%/20%)
wear_dev_split: splits dataset into 27 wear-developments
type_split: splits dataset into different BSD types
Example:
C:\Users\Desktop>python split_dataset.py --split_type=train_test_split --output_dir=BSD_split_folder
Result:
./BSD_slit_folder/train/ and ./BSD_slit_folder/test/