How Does the Maintain-AI Process Work?
Delivering The Ability to Optimise Your Road Network Assessments and Actions
The continuous inspection of a pavement network is an important element of road maintenance because surface distresses, after initiated, will cause the accelerated deterioration of a pavement's structure.
Whilst traditional approaches of distress detection are human-dependent, expensive, inefficient and/or unsafe, Maintain-AI has developed a deep learning AI solution that analyses pavement images at unprecedented accuracies.
Maintain-AI uses the most promising approaches of computer vision algorithms which adopt machine
learning models to detect the different types and severities of distresses as well as identifying and assessing numerous other associated infrastructure elements from collected road network images.
Ultimately, Maintain-AI's AI solution is extremely valuable as we are able to generate results that are considered objective and reliable (minimised standard deviations compared to suitable alternatives), effective (with minimal bias to a suitable reference observed) and efficient (when comparing the resources of cost and people required compared to comparable alternatives).
A Simple But Very Effective Process
Collect Data through your Phone and
Upload to Our Analytics Service
Pavement Defect Identification
Network Infrastructure including line markings, signage and more - not just road defects!
Defect Severity / Density
ePavement Quality Index
eRider Comfort Grade
Step 1 - Road Network Data Collection
Scan the Road Network and Upload the Data
Step 2 - Analyse the Selected Data
Use Machine Learning to Evaluate the Results
Step 3 - Reports and Dashboards
Detailed Performance Information of the Network
Time Based Asset Performance Comparisons
Implied Repair Costs
Cost-Benefit Value Ranking
Data / Platform Hosting
+ More in Development