Our team will reach out within 24 hours to gather your project requirements, clarify your business objectives, and outline the next steps in our collaboration.
Client
The client is a EU-based startup company. They use an automated approach to monitor their installations for utility companies. Their main objectives are managing assets and improving maintenance processes using fast drones.
Business Challenge
The client collects images of the objects maintained using drones. The images are collected at high speed, and not all the images contain useful information. This increases the need for a human workforce to filter the images before processing.
The business challenge was to improve the workflow and enable business scalability, automating the process without increasing the human resources involved.
Business Impact
The solution delivered significantly automated the image filtering process, making it possible to scale the business model as required.
The machine learning approach facilitated the following business benefits:
- Reducing the human resources involved and automating the process;
- Constantly improving image recognition models using the regression learning pipeline;
- Ensuring that image recognition algorithms work on resource-limited devices, making it possible to use the solution in real time without cloud computation power;
- Using cloud technologies to scale the solution.
The regression learning pipeline and online feedback to the ML model allows end-users to help improve the machine learning model quality over the time.
The recalibrated models and calculations make it possible to run visual recognition on the resource-limited devices using as little energy as possible. This enables close-to-real-time object tracking.
Technology
Caffe
DarkNet
TensorFlow
Java/Scala
Hadoop HDFS
