BSData - dataset for Instance Segmentation and industrial Wear Forecasting

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.

For more information visit the dataset-publication: Industrial Machine Tool Element Surface Defect Dataset

If you are looking for a classification dataset we recommend considering our dataset Ball Screw Drive Surface Defect Dataset for Classification.

On the left image-examples, on the right associated PNG-Annotations.

If you consider using this dataset we recommend to clone this repository.

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:

1. train_test_split: splits dataset into train and test data (80%/20%)
2. wear_dev_split: splits dataset into 27 wear-developments
3. 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/

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Ball Screw Drive Surface Defect Dataset for Classification

The dataset contains of 21835 150x150 Pixel RGB images of the surface of Ball Screw Drives. 11075 of these images are images without surface defects whereas the rest shows images with surface defects in form of so called pittings. So the dataset is evenly split over the classes. Pittings result from surface disruption and can ultimately lead to the breakdown of the component. To keep the availability of machines high it is important to find surface defects in time. The here presented dataset gives researchers and practitioners the possibility to train and test models for the classification of surface defects on machine tool elements.

Above Image is a Subset of Images with pitting. You can download this dataset here.