Roadway Crack Identification & Segmentation

The Problem:
Currently laser scanning technology has been used in the Engineering and Transportation industries to survey roads and monitor surface conditions. However, existing software that scans for road cracking requires a time-consuming pre-filtering of each data point to cut out false-identification of cracks. Also, current technology does not account for special cases such as sudden changes in elevation and man-made grooving. Additionally, existing practices involve manual labor, making them more time-consuming and subjective.

The Solution:
Researchers at the University of Alabama have developed an algorithm utilizing a deep-learning convolutional neural network and data fusion (both intensity and range images) in order to accurately and efficiently identify cracks in roads at pixel-level resolution. This unique algorithm does not require pre-filtering of each data set, which yields greater accuracy and reduces manual labor. The model detects cracks with approximately 99% accuracy, an improvement on current methods by at least 3%.


  • Higher accuracy results
  • Less time and manual labor required
  • More cost-effective and time-efficient assessment than existing laser scanning systems
  • Prevents false positive crack identification
  • Offers important insights for maintenance practices

The University of Alabama Research Office of Innovation and Commercialization (OIC) is a non-profit corpo­ration that is responsible for commercializing University of Alabama technologies and for supporting University research. At OIC, we seek parties that are interested in learning more about our technologies and commercialization opportunities, and we welcome any inquiries you may have.


Contact Information

TTO Home Page:

Name: Lynnette Scales

Title: Administrative Assistant

Department: Office for Innovation & Commercialization


Phone: (205) 348-5433