ML model for detecting contamination
Machine learning model for detecting contamination levels in tanks at manufacturing facilities
Project definition
Location
North America
Industry
Manufacturing and energy
Project type
ML model
Service list
ML Engineering / Computer Vision
Problem
The goal of this project was to develop a machine learning model capable of detecting dirt in a reservoir and determining its quantity relative to the total volume. The model was trained using manually labeled data featuring reservoirs with varying amounts of dirt
Solution
The model was successfully implemented in production and deployed on the client’s server. The solution allowed for real-time monitoring of the reservoir condition, calculating the amount of dirt present, and determining its overall percentage. With an accuracy rate of 84%, the model enabled near-complete automation in tracking reservoir contamination, as well as the setup of an automatic alert system on the terminal for the addition of cleaning agents to the water
- Python
- Azure
- Apache
- OpenCV
- numpy PyTorch
Project highlights
Results of model development
01
Detection of contamination levels
We developed an ML model to determine the amount of dirt in production tanks
02
Model accuracy is 84%
The high quality of the model significantly saves staff time and reduces the likelihood of unforeseen situations
Technologies
Python
Azure
Apache
OpenCV
numpy PyTorch
Our workflow
Development Process
In FTECH-IT we have a cohesive team of professionals across various IT fields, enabling us to handle projects end-to-end without needing external specialists. No matter how complex the project is, all you need to provide is the idea — FTECH-IT will take care of everything else
ML model for detecting contamination
Machine learning model for detecting contamination levels in tanks at manufacturing facilities
