Multi-Scale Concrete Crack Image Dataset for YOLO Detection

Images of concrete cracks form this dataset, created to support the development of a YOLOv11-based model for crack identification under high background interference. The dataset is associated with a 2026 research publication in Engineering Research Express and is provided by author Lin Wang. It is distributed as a single ZIP file with a size of approximately 27.7 MB.

Use Cases

• Training object detection models like YOLOv11 for identifying cracks in concrete surfaces.
• Benchmarking model performance on multi-scale crack detection tasks.
• Developing algorithms for crack identification in images with high background interference.
• Researching automated inspection methods for structural health monitoring in civil engineering.

Strengths

• Dataset is directly linked to a peer-reviewed research publication (Engineering Research Express, 2026).
• Released under a permissive CC-BY-4.0 license, facilitating reuse and modification.
• Explicitly designed for the challenging task of multi-scale crack detection under high background interference.

Limitations

• The exact number of images (rows) in the dataset is not specified in any source.
• No column or detailed annotation information (e.g., bounding box coordinates, class labels) is provided in the available metadata.
• Sources provide no information on the collection method, time range, or geographic origin of the images.

Provenance

Source

Lin Wang, associated with the research work 'Identification of multi-scale concrete crack under high background interference using YOLOv11-based model.'

Collection Method

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Time Range

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Freshness

2026-03-25 07:59:26

Geography

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