Steelcrack Dataset

Dataset Overview

Dataset Name

Steelcrack Dataset

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Basic Information

Image size: 512 × 512
Training set: 3,300 images
Validation set: 525 images
Test set: 530 images
Image source: Partially from the 1st International Structural Health Monitoring Competition, the rest provided by the authors. All images have been re‑annotated for finer detail.

Experimental Results

Method	mi IoU (%)	mi Dice (%)	#Params (M)	MACs (G)
U‑Net	68.49	75.13	7.77	55.01
U‑Net++	72.23	78.37	9.16	138.63
Attention U‑Net	71.25	77.54	34.88	266.54
CE‑Net	76.00	81.54	29.00	35.60
DeepLabv3+ (MobileNetv2)	68.22	71.07	5.81	29.13
DeepLabv3+ (Xception)	67.40	71.48	54.70	83.14
DeepLabv3+ (ResNet‑101)	69.04	69.45	59.34	88.84
SCRN	73.23	78.91	25.23	31.92
TransUNet	64.34	72.55	67.87	129.96
CrackSeU‑B	70.42	80.50	3.19	11.22
CrackSeU‑L	71.66	81.24	4.62	28.22
DconnNet	74.73	83.40	28.38	24.79
BGCrack V1	77.16	85.33	2.32	15.76

Citation Information

BibTeX citation:

@article{HE2024BGCrack,
  title = {Crack segmentation on steel structures using boundary guidance model},
  journal = {Automation in Construction},
  volume = {162},
  pages = {105354},
  year = {2024},
  issn = {0926-5805},
  doi = {https://doi.org/10.1016/j.autcon.2024.105354},
  url = {https://www.sciencedirect.com/science/article/pii/S0926580524000906},
  author = {Zhili He and Wang Chen and Jian Zhang and Yu-Hsing Wang},
  keywords = {Crack inspection, Deep learning, Boundary guidance method, Benchmark dataset}
}

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