IoT Demo: Detecting Potholes using Virtual Reality and ESP

[bkooman]

Potholes are the result of weakened pavement from the expansion and contraction of water over time. They can form in all sorts of different environments due to natural conditions like seasonal shifts between winter and spring, which means all roadways are subject to damages caused by potholes.

The financial impact of potholes and other effects of under-maintained roadways has a real impact on the wallets of motorists across the U.S. Each year potholes are causing an estimated $3 billion in blown tires, busted axles, and other collateral damages. A 2016 study from AAA that analyzed data from the previous five years, estimated that around 16 million drivers across the U.S. suffered damage from a pothole. As costly repairs to cars and trucks increase from the reduced budgets of state governments to repair roads, and as more drivers use highways, public roadways are in more need than ever for significant improvements.

According to the same AAA survey, lower-income individuals are most affected by potholes damages because oftentimes a costly bill can have a substantial impact on their financial stability. Once they suffer damage, they are less likely to repair their vehicles, adding to the potential for unsafe driving conditions for themselves as well as for other motorists. Damage to vehicles causes other problems too as damaged cars often pull over to the side of the road where they can sit for long periods of time before removal. This increases the probability of vehicular accidents, and increases traffic congestion and gas consumption.

Municipalities have developed some ways to prevent potholes from forming in city streets like crack sealing, road patching, and overlays, and these preventative efforts can help reduce damage to vehicles and costly roadway repairs. The main problem is that the same municipalities don't have ways to timely identify the areas that need intervention.

This demo shows how visual detection technology can dramatically help cities keep road maintenance costs down and improve overall public safety by identifying and ranking potholes based on their condition so that repairs can be pre-emptively executed. The solution is cost-effective because it uses cameras mounted on garbage trucks, which already drive through cities to pick up waste. As they follow their routes, each pothole they find is scored and identified via GPS coordinates. This information is used to feed an ESP project and a visualization component that displays the potholes on a map.

Click here to watch a video overview of the demo to learn more.  

[sbxkoenk]

Hello,

This makes me think about this blog :

No more ‘leaves on the line': Is computer vision the answer for rail and transport networks?
By Mark Frankish on Hidden Insights 31 March 2020
https://blogs.sas.com/content/hiddeninsights/2020/03/31/no-more-leaves-on-the-line-is-computer-visio...

Cheers,

Koen