ULC Robotics’ AIM service combines machine learning and a vehicle-mounted data collection system to autonomously generate an interactive visual database of utility poles and assets, create GIS mapping and conduct inspection of the overhead electric distribution system to improve reliability and asset management while lowering costs.
Powered by a trained deep learning model, AIM processes visual data in real-time and identifies different utility assets with higher than 98% accuracy. Further use of the model will increase its accuracy.
Using image processing and machine learning through a patent-pending technique, the system accurately measures the location of each identified asset and creates a global map of the inspected area. These results can also, be integrated into any GIS mapping system.
Through our patent pending technology, AIM accurately measures the leaning angle of the poles and provides data analytics on the status of the network and its the integrity of the poles.
The development team at ULC Robotics can further train the deep learning models so that it can identify and inspect additional assets or integrate additional sensor data to accommodate the immediate needs of utility owners.
AIM can accurately identify and inspect assets, eliminating the need for manual data review and reducing the burden on SMEs.
The raw data as well as identification and inspection results are stored on a secure cloud-based storage through which AIM user interface provides asset owners with analytical reports on the status of the network.