Industrial machine tool component surface defect dataset

Schlagenhauf, Tobias; Landwehr, Magnus

doi:10.1016/j.dib.2021.107643

Cited by 15 publications

(10 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Five datasets are exerted in this study, that is, four public datasets (RSDDs [ 41 ], BSData [ 42 ], NRSD-MN [ 36 ], and NEU-DET [ 38 ]) and one internal dataset for comparison with existing methods. The internal dataset is the image of the mold point at the bottom of the glass bottle, which is collected and saved from the actual production line with a CCD camera.…”

Section: Resultsmentioning

confidence: 99%

A Model for Surface Defect Detection of Industrial Products Based on Attention Augmentation

Shao

Wan

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Detecting product surface defects is an important issue in industrial scenarios. In the actual scene, the shooting angle and the distance between the industrial camera and the shooting object often vary, which results in a large variation in the scale and angle. In addition, high-speed cameras are prone to motion blur, which further deteriorates the defect detection results. In order to solve the above problems, this study proposes a surface defect detection model for industrial products based on attention enhancement. The network takes advantage of the lower-level and higher-resolution feature map from the backbone to improve Path Aggregation Network (PANet) in object detection. This study makes full use of multihead self-attention (MHSA), an independent attention block for enhancing the backbone network, which has made considerable progress for practical application in industry and further improvement of the surface defect detection. Moreover, some tricks have been adopted that can improve the detection performance, such as data augmentation, grayscale filling, and channel conversion of input images. Experiments in this study on internal datasets and four public datasets demonstrate that our model has achieved good performance in industrial scenarios. On the internal dataset, the mAP@.5 result of our model is 98.52%. In the RSDDs dataset, the model in this study achieves 86.74%. In the BSData dataset, the model reaches 82.00%. Meanwhile, it achieves 81.09% and 74.67% on the NRSD-MN and NEU-DET datasets, respectively. This study has demonstrated the effectiveness and certain generalization ability of the model from internal datasets and public datasets.

show abstract

Section: Resultsmentioning

confidence: 99%

A Model for Surface Defect Detection of Industrial Products Based on Attention Augmentation

Shao

Wan

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…The dataset for classification, detection, and forecasting is available in (Schlagenhauf & Landwehr et al 2021). The trained detection model is available under: https://github.com/2Obe/BSData This work was supported by the German Research Foundation (DFG) under Grant FL 197/77-1.…”

Section: Discussionmentioning

confidence: 99%

Industrial Machine Tool Element Surface Defect Dataset

Schlagenhauf¹,

Landwehr²,

Fleischer³

2021

Self Cite

View full text Add to dashboard Cite

Using machine learning (ML) techniques in general and deep learning techniques in specific needs a certain amount of data often not available in large quantities in technical domains. The manual inspection of machine tool components and the manual end-of-line check of products are labor-intensive tasks in industrial applications that companies often want to automate. To automate classification processes and develop reliable and robust machine learning-based classification and wear prognostics models, one needs real-world datasets to train and test the models. The dataset is available under

show abstract

“…The data used in this article is taken from the Karlsruhe Institute of Technology in Germany, and the data comes from the ball screw surface defect data set released by the institute in 2021 [10] .…”

Section: Dataset Processingmentioning

confidence: 99%

Surface pitting detection of ball screw based on deep learning

Wei,

2024

International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023)

View full text Add to dashboard Cite

In view of the fact that most of the existing research on the pitting failure of ball screw focuses on the vibration signal experiment and the establishment of the degradation model, and the use of more intuitive visual aspects is less, the deep learning is studied in the pitting detection of the surface of the ball screw. The application of Faster R-CNN and Mask R-CNN two network models were built, and the two were compared and analyzed through experiments. The results show that both Faster R-CNN and Mask R-CNN can guarantee high classification accuracy under different learning rates, and both can excellently complete the detection task of pitting on the surface of the ball screw. While locating the eclipse, the mask of the pitting is output synchronously, which is helpful in the face of the subsequent task of describing the size of the pitting, and has more advantages in the face of small-area pitting, and there are fewer missed detections.

show abstract

Industrial machine tool component surface defect dataset

Cited by 15 publications

References 8 publications

A Model for Surface Defect Detection of Industrial Products Based on Attention Augmentation

A Model for Surface Defect Detection of Industrial Products Based on Attention Augmentation

Industrial Machine Tool Element Surface Defect Dataset

Surface pitting detection of ball screw based on deep learning

Contact Info

Product

Resources

About