Artificial intelligence is emerging in the wastewater industry, and it has the power to transform everything we know. The hopeful benefits of AI over traditional operator inspections are mostly due to lower error rates and faster inspection times.

The biggest obstacle in the way of artificial intelligence making a positive impact in the way we conduct sewer inspections is the learning curve – the MACHINE learning curve. As with any machine-driven technology, learning and assessing is a critical step to define the parameters of a defect. It is estimated that most AI technologies require a minimum of 1 million feet of inspection footage to train systems and ensure accuracy1.

Inadequately trained software will make mistakes, confusing small roots for cracks, holes for taps, and cracks for fractures. Under-coding a defect, marking it as less severe than it is, means serious issues may go ignored. Over-coding, marking a minor defect as more severe, means defects that don’t need immediate repair may clutter the top of the to-do list.

Having reliable access to inspection footage is the first step towards adopting AI technology. T4 Vault stays ahead of this ever-changing technology. T4 Vault allows municipalities to upload all of their data to a secure, cloud-based platform to catalog, store, link to GIS, and share with ease. Moving inspection data onto the system is as simple as a fast drag and drop of a file.

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11 Lygo, N. (2019, August 22). The Future of Defect Coding: Artificial Intelligence in Envirosight. Retrieved from https://blog.envirosight.com/the-future-of-defect-coding-artificial-intelligence

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