Wapice has been involved in the development of a neural network-based road damage detection solution for Destia, which will help the company improve road condition monitoring and effectively anticipate the necessary maintenance resources. With the help of diverse data, road damage can be detected faster and more accurately, which means that any damage can be acted upon before it causes harm to road users.
The solution designed by Wapice uses the latest available methods to achieve a more accurate, quicker and error-free image processing result than in the case of traditional methods. In addition, the developed neural network-based model can be used in diverse environments without a need to adjust parameters.
– The solution for detection of road damage is based on convolutional neural networks that have an established place in modern image processing. They are particularly used for such challenging detection and classification tasks, says Mickey Shroff from Wapice.
Read the full story of the collaboration and the benefits achieved with the solution.
Destia is a Finnish infrastructure and construction service company that builds, maintains and designs not only traffic routes, railways and industrial and traffic environments but also entire milieus. In Finland, Destia is responsible for the maintenance of approximately 45,000 km of the road network.
- Wapice Oy, Mickey Shroff, Head of AI, Analytics & Automation Solutions Segment, +358 50 302 7187, email@example.com
- Destia Oy, Arto Kuskelin, Head of Infrastructure Management Unit, +358 40 546 0126, firstname.lastname@example.org
Article image: ©Destia