What is Crack Segmentation?
Crack segmentation is a computer vision task that labels each pixel of an image as crack or not crack. The output is a mask with the same size as the image. Crack pixels are set to 1 and all other pixels to 0.
How it differs from classification and detection
Classification gives one label for the whole image. Detection draws boxes. A box around a thin, winding crack is mostly background and says nothing about the crack’s width or branching. A mask keeps the shape of the crack.
What the mask gives
If the size of one pixel on the surface is known, the mask gives measurements:
- Area: the number of crack pixels multiplied by the area of one pixel.
- Length: the length of the one-pixel centreline of each crack, found by thinning the mask.
- Width: the distance from the centreline to the crack edge, doubled.
A coarser pixel size means that hairline cracks fall below one pixel and are lost. The pixel size on the surface depends on the camera and the distance to the pavement.
Models and data
Most crack models are encoder-decoder networks. The U-Net is the usual starting point. It has a contracting path that captures context and an expanding path that restores position. It trains from few images (U-Net paper ).
Public training sets are small and mostly show roads or campus pavement. DeepCrack has 537 annotated images of asphalt and concrete. CrackForest has 118 annotated road images, and CRACK500 has 500 phone images of pavement. CrackSeg9k combines several such sets. A model trained on them has not seen a grooved runway, so it needs checking on airfield images before its output is trusted.
What it does and does not do on an airfield
EASA guidance lists three pavement damage signs to look for during an inspection. They are cracking and spalling of concrete, the condition of joint sealing, and cracking of asphalt. It also notes that cracked and spalled pavement can shed pieces that become foreign object debris.
A mask is a measurement input, not a rating. In the PAVER method, the severity of a longitudinal or transverse crack in asphalt depends on spalling and filler condition. For a non-filled crack, a mean width above 6 mm also raises it to medium. Length is recorded in linear feet or metres per severity level.
Two airfield features need separate handling:
- Grooves: saw-cut grooves are transverse, 6 mm wide, 6 mm deep and 38 mm apart on a runway. A model has to tell these regular lines from cracks.
- Joints: concrete panels have joints at set spacing, between 3.8 m and 6.1 m depending on panel thickness. Joints are designed features and are not cracks, although the condition of the joint seal is inspected.
Frequently Asked Questions
- How is crack segmentation different from crack detection?
- Segmentation marks the exact pixels of each crack. Detection draws a box around a crack, and classification only says whether an image contains a crack. The mask is what lets software measure a crack's length and width, because a box around a thin crack is mostly background.
- Does a crack mask give the severity of a crack?
- No. A mask gives the position, length and width of a crack. In the PAVER method for asphalt airfields, the severity of a longitudinal or transverse crack also depends on spalling and crack filler condition. An unfilled crack with a mean width above 6 mm is at least medium severity. A narrow crack is high severity if it is badly spalled.
- Which datasets are used to train crack segmentation models?
- Common public sets are DeepCrack (537 images of asphalt and concrete), CrackForest (118 annotated road images from Beijing) and CRACK500 (500 pavement images from phones). CrackSeg9k combines several existing sets. These are mostly road and campus images, not runways.
References
- unet
- U-Net: Convolutional Networks for Biomedical Image Segmentation Ronneberger, Fischer and Brox, arXiv 1505.04597
- deepcrack
- DeepCrack: Learning Hierarchical Convolutional Features for Crack Detection (Neurocomputing, 2019) Liu et al., via secondary summaries (hyper.ai, cris.fbk.eu)
- crackseg9k
- CrackSeg9k: A Collection and Benchmark for Crack Segmentation Datasets and Frameworks arXiv 2208.13054
- cfd-crack500
- CrackForest and CRACK500 dataset descriptions (survey summaries found by web search) arXiv 2507.06513 and other surveys
- paver-asphalt
- PAVER Distress Identification Manual for Asphalt Surfaced Airfields (June 2009) US Army ERDC-CERL
- easa-ear-adr
- Easy Access Rules for Aerodromes (Regulation (EU) No 139/2014) EASA
- faa-ac-150-5370-10h
- AC 150/5370-10H, Standard Specifications for Construction of Airports FAA
- faa-ac-150-5320-6g
- AC 150/5320-6G, Airport Pavement Design and Evaluation FAA
This is a reference entry. TarmacView provides drone inspection services and doesn't sell the equipment or materials described in this glossary.


