Few-shot Surface Defect Datasets

1.Classification (Cls):

1.1 Classification-Strip Steel: FSC-20 Dataset

We constructed a large-scale few-shot classification dataset named FSC-20(few-shot classification contains 20 types of strip steel surface defects). This dataset was collected from the three datasets the NEU-CLS, the X-SDD, and the GC10-DET, with a total of 20 categories. Specifically, it includes 10 types of cold-rolled defects (all from GC10-DET) and 10 types of hot-rolled defects (6 types from NEU-CLS and the other 4 types from X-SDD).

Wenli Zhao,  Kechen Song,  Yanyan Wang, Shubo Liang, Yunhui Yan. FaNet: Feature-aware Network for Few Shot Classification of Strip Steel Surface Defects [J].  Measurement,  2023, 208, 112446. (IF:5.6)  (paper)  (Dataset and code) 

1.2 Classification-Metal: MSD-Cls Dataset

We constructed a few-shot metal surface defect dataset named MSD-Cls. The MSD-Cls dataset includes ten types of steel surface defects and ten types of aluminum surface defects. The ten types of steel surface defects are from Northeastern University classification (NEU-CL) datasets and railway surface defect datasets (RSDDs). Each type of steel defect contains 50 images of size 224 × 224. The ten types of aluminum surface defects come from the dataset provided by the 2018 Tianchi competition, the size of these images is 256×256, and the number of pictures in each type ranges from 22 to 48.

Weiwei Xiao, Kechen Song, Jie Liu and Yunhui Yan.Graph Embedding and Optimal Transport for Few-Shot Classification of Metal Surface Defect [J]. IEEE Transactions on Instrumentation and Measurement, 2022, 71,5010310.(IF:5.6)   (paper) (Dataset and code) 

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2.Semantic Segmentation:

2.1 Semantic Segmentation-Strip Steel: FSSD-12 Dataset

We constructed a novel few-shot segmentation dataset, FSSD-12, to address the severely insufficient pixel-wise labeled S3D samples in existing works. There are twelve S3D classes in FSSD-12, including abrasionmask, iron-sheet ash, liquid, oxide-scale, oil-spot, water-spot, patch, punching, red-iron sheet, roll-printing, scratch, and inclusion. All raw defective images are integrated from DET GC-10, X-SDD, SD-900, and Surface Defects-4i , which are taken in the production and manufacturing stages. Subsequently, we meticulously annotate the overall defective images with pixel-wise labels.

Hu Feng, Kechen Song,  Wenqi Cui, Yiming Zhang, Yunhui Yan. Cross Position Aggregation Network for Few-shot Strip Steel Surface Defect Segmentation [J]. IEEE Transactions on Instrumentation & Measurement, 2023, 72, 5007410. (IF:5.6)    (paper) (Dataset and code) 

2.2 Semantic Segmentation-Metal: Surface Defects-4i Dataset

We constructed a novel dataset, Surface Defects-4i , that uses images and annotations of multiple metal surface defect dataset. In addition, we also add two classes of nonmetal as an extension and further prove the generality of our method. Specifically, before experimenting, we grayscale all images and uniform size to 200 × 200 for ensuring consistency. There are a total of 12 different classes of surface defects in the dataset. Each class includes defective images, groundtruth (GT), and a large number of normal images.

Yanqi Bao,  Kechen Song, Jie Liu, Yanyan Wang, Yunhui Yan, Han Yu, Xingjie Li. Triplet- Graph Reasoning Network for Few-shot Metal Generic Surface Defect Segmentation [J].IEEE Transactions on Instrumentation and Measurement, 2021, 70, 5011111(IF:5.6)    (paper)  (Dataset and code)

2.3 Cross-granularity few-shot defect segmentation-9: CGFSDS-9 Dataset

We construct a novel benchmark, named cross-granularity few-shot defect segmentation-9 (CGFSDS-9), which includes a coarse-grained labeled subclass for meta-training and fine-grained labeled subclass for meta-testing.

Kechen Song,  Hu Feng, Tonglei Cao, Wenqi Cui, Yunhui Yan.  MFANet: Multifeature Aggregation Network for Cross-granularity Few-shot Seamless Steel Tubes Surface Defect Segmentation [J]. IEEE Transactions on Industrial Informatics, 2024, 20(7), 9725-9735. (IF:11.7)   (paper)   (Dataset and code)