Crack Segmentation
13 papers with code • 2 benchmarks • 3 datasets
Crack segmentation in computer vision involves identifying and delineating cracks or fractures in various types of surfaces, such as roads, pavements, walls, or infrastructure. This task is crucial for infrastructure maintenance, as it helps in assessing the condition of structures and planning repairs.
Benchmarks
These leaderboards are used to track progress in Crack Segmentation
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
CRACK500
Khanhha's dataset
Most implemented papers
Real-time High-Resolution Neural Network with Semantic Guidance for Crack Segmentation
CHDyshli/HrSegNet4CrackSegmentation
•
•
Deep learning plays an important role in crack segmentation, but most work utilize off-the-shelf or improved models that have not been specifically developed for this task.
CrackNex: a Few-shot Low-light Crack Segmentation Model Based on Retinex Theory for UAV Inspections
zy1296/cracknex
•
•
LCSD consists of 102 well-illuminated crack images and 41 low-light crack images.
Segmentation tool for images of cracks
Machine learning algorithms can be used for augmenting the capability of classical visual inspection of bridge structures, however, the implementation of such an algorithm requires a massive annotated training dataset, which is time-consuming to produce.
