A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects
By Kechen Song, Yunhui Yan. 2013
887 citations recorded by the source corpus.
Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering
View Paper: https://www.semanticscholar.org/paper/a3b041214f751557b10d63b9c0f753f75bf5d1b2
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In Computational Mechanics
In Computer Vision and Pattern Recognition
In Industrial and Manufacturing Engineering
Citations in the corpus
- Deep Industrial Image Anomaly Detection: A Survey (2023, neutral): “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”
- Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks (2018, background): “These machine vision-based inspection systems occur in many industrial applications, such as steel strip inspection [2,3], liquid crystal display (LCD) inspection [4], fabric inspection [5,6], aluminum profiles [7], railway track inspection [8], food inspection [9], and optical components inspection”
- A Generic Deep-Learning-Based Approach for Automated Surface Inspection (2018, background): “The first public database is the NEU surface defect [29].”
- RetinaNet With Difference Channel Attention and Adaptively Spatial Feature Fusion for Steel Surface Defect Detection (2020, methodology): “It seems that nine kinds of anchors in RetinaNet are enough, but not for some specific defect data, for example, the steel surface defect data set [31].”
- A Survey of Deep Active Learning (2020, methodology): “Object detection [178] VOC [59], Kitti [77] – [110, 154] SS [215], eMML [67], NACTI,1 CCT,2 UAV3 Biodiversity survey [63] KITTI [78] Autonomous driving [140] NEU-DET [209] Defect detection Semantic segmentation [74, 232, 233, 243] SPIM [46], Confocal[47], LIDC-IDRI [12], MICCAI, Lymph node [252] Bio”
- A Survey on Few-Shot Class-Incremental Learning (2023, result): “They convert NEU-DET (Song and Yan, 2013) into an incremental few-shot dataset, and the ex-periment shows that they achieve great performance compared to other methods.”
- An End-to-End Steel Surface Defect Detection Approach via Fusing Multiple Hierarchical Features (2020, methodology): “For instance, Song and Yan [3] improve the LBP to against noise and adopt NNC and SVM to classify defects.”