The technology, developed by a team led by NTU research fellow, Senthan Mathavan, scans roads for 'ravelling' - the loss of aggregates from the asphalt which leads to potholes and cracks.
Combined with 2D and 3D scanners on a pavement monitoring vehicle, a computer vision algorithm can examine the road with accuracy at traffic speed during day or night.
The system works by detecting different textures of the road to identify ravelling and distinguishes it from shadows and blemishes such as tire marks, oil spills and recent pothole repairs.
“It’s imperative for authorities across the world to be able to monitor road conditions efficiently and safely,” says Dr Mathavan, a research fellow of NTU's School of Architecture, Design and the Built Environment.
“For the first time, academic research has addressed the issue of detecting ravelling in an automated way, which has led to the development of this novel software which can be used across the industry.”
The research is published today (January 27) in the journal, Transportation Research Record. It also involves Dr Mujib Rahman of Brunel University, Martyn Stonecliffe-Jones of Dynatest UK, and Dr Khurram Kamal of the National University of Sciences and Technology in Pakistan.
During the research, the team found that the technology detected road surfaces correctly in all 900 images tested. It took approximately 0.65 seconds to 3D process the ravelling measurements, but it is believed that this could be reduced further.
“Potholes, in their worst potential form, can create dangerous driving conditions and cause costly damage to vehicles," adds Dr Rahman. “What this technology allows us to do is capture better quality information on road conditions, without disrupting the flow of traffic or incurring unnecessary costs. This could be a significant step forward in the way that potholes are managed, helping improve the timeliness and efficiency of repairs.”