Surface defect saliency of magnetic tile
By Yibin Huang, Congying Qiu, Kui Yuan. The Visual Computer, 2018
533 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering
View Paper: https://www.semanticscholar.org/paper/a62b0b8ff07bdbcf4fd1c8449acac1f24d8434c4
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Jitendra Malik
- Bernt Schiele
- Andrew Zisserman
- Dacheng Tao
- Ming-Hsuan Yang
- Xiaogang Wang
- Cordelia Schmid
In Industrial and Manufacturing Engineering
- Kilian Batzner
- Lars Heckler
- Rebecca König
- Latha Pemula
- David Sattlegger
- Paul Bergmann
- Michael Fauser
- Carsten Steger
Citations in the corpus
- PGA-Net: Pyramid Feature Fusion and Global Context Attention Network for Automated Surface Defect Detection (2020, background): “MT Defect Dataset: The magnetic-tile defect dataset is presented in [48] which contains 1344 defect images, and each raw defectimagecorrespondstoapixel-levellabel.”
- EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies (2023, background): “Recently, several new industrial anomaly detection datasets have been introduced [8, 11, 26, 28, 38, 73].”
- The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection (2021, background): “Huang et al. (2018) introduce a surface inspection dataset of magnetic tiles.”
- Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows (2020, methodology): “Magnetic Tile Defects [14] comprises grayscale images of magnetic tiles under different illuminations with and 0.”
- Surface Defect Detection Methods for Industrial Products: A Review (2021, unknown): “Magnetic tile surface defects [121] Tile https://www.”
- Deep Industrial Image Anomaly Detection: A Survey (2023, unknown): “Dataset Class Normal Abnormal Total Annotation Level Real or Synthetic AITEX [138] 1 140 105 245 Segmentation mask Real BTAD [97] 3 2,830 Segmentation mask Real DAGM [139] 10 11,500 Segmentation mask Synthetic DEEPPCB [140] 1 1,500 Bounding box Synthetic Eycandies [141] 10 13,250 2,250 15,500 Segmen”
- Towards Total Recall in Industrial Anomaly Detection (2021, methodology): “Thorough experiments on the diverse MVTec AD [5] as well as the specialized Magnetic Tile Defects (MTD) [26] industrial anomaly detection benchmarks showcase the power of PatchCore for industrial anomaly detection.”