AI Goes Underground: Computer Vision From SewerAI Classifies Defective Pipes

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Computer vision from SewerAI identifies flaws in water pipes.

Computer vision systems are surveying sewers for signs of decay and degradation.
What’s new: A system from California startup SewerAI analyzes videos of underground pipes to prioritize those in need of repair.

How it works: SewerAI’s computer vision system classifies defects like cracks, holes, displacements, tree roots, and incursions in videos taken by sewer-crawling robots and human inspectors.

  • The system was trained on 100,000 videos taken during sewer pipe inspections, amounting to about 3 million minutes of imagery, CEO Matthew Rosenthal told The Batch.
  • The company serves dozens of clients, largely cities or their contractors, across the U.S. and parts of Canada. It enables HK Solutions Group to inspect 200,000 feet of pipe monthly and complete tasks in one day that formerly required weeks or months, an HK representative told The Wall Street Journal.

Behind the news: AI is doing the dirty work for a growing number of companies.

  • DC Water, the water utility in the U.S. capital, collaborated with Intel and the information-technology company Wipro to develop a fully automated pipe inspector. Their Pipe Sleuth identifies defects in videos captured by autonomous crawlers made by Pennsylvania-based RedZone Robotics.
  • IBAK, a German maker of pipe-inspection systems, is training a defect classifier on data supplied by users of its camera system.

Why it matters: Failed pipes can cause flooding, spread disease, and pollute water sources. In 2019, the American Society of Civil Engineers estimated the cost of shoring up the U.S. wastewater infrastructure at $129 billion — at least $81 billion more than lawmakers allocated in a recent law. By helping human inspectors prioritize repairs, computer vision could help stretch those dollars across more miles of pipe.

We’re thinking: Would we rather let a robot inspect sludge-filled pipes than do it ourselves? Sewer we would!

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