Why Municipalities Are Investigating AI Sewer Analysis

  • AI-powered sewer diagnostics removes the need for Sewer Televising Operators to go through paid continuing education regarding sewer defect identification.
  • As staff either retires or leaves Municipalities, Automated Sewer Defect Detection shortens the learning curve for both Sewer Televising Operators and Consulting or Inhouse Engineers regarding sewer defect detection.
  • Sewer AI inspection technology makes presentations to Cities, Villages, Town Boards and Sanitary Districts, quicker, easier and visually self-explanatory to whomever is viewing the presentation.
  • AI-powered sewer diagnostics provides more robust information when requesting funding for Sewer Collection System Pipe Rehabilitation, Repair or Replacement.  Automated sewer defect detection visually displays sewer defects using a sewer defect bounding box format for ease of identification.
  • This Patented AI Technology, by Infrastructure DL, is delivered in an Excel Format.  Excel is a very familiar format within the Wastewater Collection System Community.
  • Existing digital sewer televising videos can be processed from as far back as 20 years ago or as recent as yesterday.  This allows Municipalities to take advantage of Collection System Digital Sewer Televising that has already been paid for, but has not been analyzed.
  • Easy on Budgeting.  No Specialized Software Required.  Billed only by the lineal foot with no Minimums.

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5 Ways AI Improves Sewer Video Inspection Accuracy

  1. Remove Sewer Televising Operator Bias
    • Often times Sewer Camera Operators retain Bias from the last project they recently Televised.  If an operator just completed a project where Roots were very prevalent, roots are on the operator’s mind and the sewer televising operator may be seeking out Roots more than other types of pipe defects.  AI algorithms have no residual bias.  Defects are marked based on trained algorithms of several thousand sewer defect types and categories.
  2. AI-powered sewer diagnostics are more thorough than manual sewer defect detection
    • Sewer Televising Operators typically do not mark every defect within a sewer pipe.  Often Operators will mark a particular defect and use a defect extension “Continual Defect”. The “Continual Defect” technique diminishes the visual accuracy of the projected overall pipe condition.  AI sewer defect detection marks far more defects than the “Continual Defect” technique. Automated sewer defect detection contributes to a more robust overall pipe condition assessment report.
  3. Increased Operator Focus on Sewer Camera Operation
    • Currently most Sewer Televising Operators use software that requires action when encountering and identifying a defect.  Typically, an operator must select a defect severity level from a computer pull down screen menu.  This technique removes focus and attention from the sewer camera within the sewer pipe.  AI sewer defect detection eliminates the need for removing focus from the operation of the $30,000 plus value of sewer camera equipment inside the pipe.  Focusing mostly on operating the sewer equipment, within the sewer pipe, contributes to increased operator skill level and possibly reduced equipment repair expenses.
  4. Lessens Operator Production Pressure
    • Many Sewer Televising Operators feel the pressure of televising a required minimum amount of footage daily.  Logging sewer defects manually takes time. Automated sewer defect detection speeds up sewer televising, by mitigating the data entry time and increasing sewer televising camera movement time.
  5. Eliminates Consulting Engineer Evaluation Bias
    • Many municipalities use consulting Civil Engineers or in house municipal staff Engineers.  When Engineers evaluate sewer televising video, they encounter similar biases that sewer televising operators encounter.  When Engineers see the designation “Continual Defect” it forces the Engineer to watch the entire sewer televising video. Manual sewer televising video evaluation is a time consuming and tedious job.  Defect identification bias is often times a byproduct of evaluating sewer televising videos.  AI sewer defect detection mitigates sewer video evaluation bias and fatigue.
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