The Accuracy of AI Fault Detection

By Mike Ball / 06 Apr 2021
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Scopito has released an article explaining why AI fault detection models, such as those used by the company to derive insights from drone inspection data, may not be 100% accurate, and why this may not necessarily be an issue. According to Scopito, the real value of implementing AI into a workflow is not the higher levels of accuracy but the savings on labour.

Read the full article on Scopito’s website here

The article covers:

  • Factors that impact the accuracy of fault detection, including poor data quality, poor conditions such as low light or bad weather, and human issues
  • The importance of training fault detection models with a diverse data set, and of using an experienced and knowledgeable operating crew
  • The ways in which the analysis speed of an AI model saves resources
  • How to get started with AI asset fault detection

Scopito provides customers with solutions that allow AI models to be trained by the customer with already annotated data, and also provides AI specialists who can complete the training and any modifications needed. To find out more about AI fault detection accuracy for drone inspections, read the full article on Scopito’s website.

Posted by Mike Ball Mike Ball is our resident technical editor here at Unmanned Systems Technology. Combining his passion for teaching, advanced engineering and all things unmanned, Mike keeps a watchful eye over everything related to the unmanned technical sector. With over 10 years’ experience in the unmanned field and a degree in engineering, Mike’s been heading up our technical team here for the last 8 years. Connect & Contact