The US has over four million miles of roads and nearly half are in poor or mediocre condition, according to the American Society of Civil Engineers (ASCE)'s most recent report card for America's infrastructure.
Our safety, economy, and overall health depends on roads in good condition; however, after a road’s initial construction, traffic, rain, snow, and temperature changes can severely impact its surface, causing potholes, cracking and other imperfections that negatively impact driving performance. The road inspections needed for ongoing maintenance are typically infrequent since assessing roads manually is a cumbersome, slow process that requires a team of inspectors to walk or drive along roads, physically examine the pavement, and stop to note defects. While these types of inspections can take weeks or even months to finish, road conditions continue to deteriorate. Manual road pavement assessments can't keep up with the rapid pace of deterioration.
Poorly maintained roads affect all of us – motorists, pedestrians, and the general public. Their effects include:
Artificial intelligence (AI) is key to the future of road maintenance, providing an opportunity for roadway owners, maintainers, and transportation engineers to optimize their pavement assessment processes and move away from manual inspections. Data can be easily and quickly collected from cameras installed on vehicles or smartphones mounted on windshields/dashboards. Then, computer vision models can analyze the road imagery and instantly recognize and record asphalt pavement defects, such as cracks, ruts, and potholes.
Machine learning models are designed to analyze data from various sources and predict when roads are likely to deteriorate, delivering rapid insights on large road networks. This is critical to facilitate proactive maintenance. Traditional reactive maintenance approaches address issues only after they have become significant problems. With the use of AI, potential issues are identified early, enabling timely interventions that prevent further deterioration. This proactive approach extends the lifespan of installed pavements and optimizes maintenance budgets by addressing problems before they escalate.
By automating the defect detection process, AI not only speeds up assessments, but also improves accuracy and reduces the likelihood of missed defects.
Although AI technology is rapidly developing and gaining traction for asphalt pavement analysis, it hasn't been universally adopted due to concerns about accuracy, costs, and perceived technical complexity. While it's natural to be wary of the unknown, municipalities and other government agencies shouldn't let their initial hesitations stop them from exploring AI's potential. Using AI to automatically detect defects in asphalt roads can be a value-add service that supports the expertise of skilled inspectors, optimizing their work without replacing their invaluable judgment.
By being able to collect data and objectively analyze it with greater precision and speed, municipalities can have a comprehensive view of their roadways and more effectively budget/plan for potential maintenance work. Better roads will impact the entire community, resulting in:
We've recently introduced a computer vision model for asphalt defect detection. Available through our powerful BRYX platform, the Asphalt Defect Detection model is designed to analyze road imagery and detect pavement defects such as cracks and potholes, providing cities with valuable data to plan and budget for maintenance more effectively.
Try our free beta version today! Share your feedback on the model's performance and gain advanced insights into road defects using your own data.