Overview & features
The electric grid is old and deteriorating. With rising incidences of extreme weather events that put our grid at risk of extended power outages and blackout periods, it is more critical than ever for utilities to enact mitigation measures to improve grid reliability, resiliency & safety.
For the distribution grid, this is a tall order. Utility poles are dispersed across wide geographic areas, with large numbers of assets that require regular evaluations. Currently, these inspections are conducted via dedicated crews and truck patrols that manually inspect assets using binoculars, handheld cameras and paper—a process that is slow, costly and unscalable.
Noteworthy AI’s Inspector Edge helps to solve this challenge by employing vehicle-mounted cameras and AI to help utilities collect and analyze distribution asset data at-scale. These smart cameras, powered by proprietary computer vision models, enable Inspector Edge to mitigate risk and significantly reduce operations and maintenance costs across a variety of use cases, including: geolocation and GIS clean-up, asset inspection, vegetation management, joint use compliance, and unregulated lighting assessments.
Vehicle-mounted smart cameras
Noteworthy AI’s Inspector Edge solution comprises an integrated system of computer vision cameras, edge computing devices, and proprietary AI models that easily mount on existing fleet vehicles.
Automatic pole GIS & imagery collection
Inspector Edge’s smart cameras are equipped with asset management and inspection software designed to automatically collect utility asset data while the vehicles drive during routine operations. No operator intervention required.
Real-time analysis & alerts
Inspector Edge’s integrated compute unit performs real-time analysis as the cameras capture pole imagery, enabling immediate notification of equipment defects and reduced downstream cloud processing costs.
LiDAR-like accuracy without the cost
We employ cutting edge techniques that combine stereo vision point clouds and neural nets to generate high-accuracy 3D reconstruction images. Inspector Edge can achieve LiDAR-like results without steep time and cost commitments.
Applications
By regularly collecting high-quality utility pole images along with respective GIS data, we can customize our machine learning models to accommodate a wide variety of use cases including, but not limited to:
- Asset inventory: easily maintain an up-to-date repository of your distribution assets’ geographic coordinates & pole-top components
- Asset inspection: automatically identify utility pole defects and notify the appropriate parties
- Vegetation overgrowth: enable proactive pruning maintenance for areas with extensive overgrowth
- 3rd party attachments: track 3rd party equipment and ensure NESC compliance with LiDAR-like accuracy
- Unregulated lighting assessments: inventory and assess pole-mounted lighting components
Benefits
Improve grid reliability, resiliency & safety
Real-time analysis means immediate notification of equipment defects. This directly shortens the duration of the inspect-review-remediate cycle.
Increase situational awareness
Inspector Edge enables at-scale asset data collection and analysis, allowing for a comprehensive understanding of asset locations and conditions across the entire network.
Reduce O&M costs
Reduce the number of grid patrols / truck rolls dedicated to pole evaluations. Inspector Edge is active on all vehicles, all the time - even during routine operations.
Seamless integration with existing systems
Collected data are easily imported into existing asset management and GIS systems, or can be automatically sent to Inspector Cloud, which enables quick and easy review of defects, inventories and more.
Leverage existing fleet vehicles
Inspector Edge non-intrusively mounts on fleet vehicles, enabling further value extraction from existing capital investments.
Keep your data safe & secure
All data collected by Inspector Edge are fully encrypted both at-rest and in-transit with industry leading AES-256 to ensure maximum security.