Artificial intelligence is an algorithmic genre based on imitation of human intellectual processes. It has many uses in different fields, and the most popular of them is computer vision.
Since computer vision AI algorithms are composed from a large set of relatively simple computer vision core elements, it takes the classic computer vision approach to a whole new level. Once an AI algorithm is put together from these building blocks it is called a model. Each of these building blocks is referred to as a layer and all layers are commonly connected in a preceding formation.
One can think of an AI model flow as the process of an infant growing up. It is exposed to a lot of new information, such as sights, sounds and other sensory signals. It collects some impressions from these experiences, in an inner coded form (features), makes different kinds of connections between them, and eventually draws conclusions regarding a new given input. In this way, an AI model relies on previous ‘memories’ learned since it was ‘born’, which gives it unique human-like qualities for a software.
AI is commonly referred to as Deep Learning, due to its multiple layers for extracting features, where each layer is ‘fed’ from previous layers’ outputs. In that manner, each layer produces some extra information regarding the input and the deeper you go, it gets more and more encrypted.
AI Brings Immediate ROI for Asset Inspections
Enterprises are currently undergoing a massive digital transformation. One of the areas that is impacted is the way businesses perform asset inspections. Enterprises need to get rapid insights for a large scale of assets.
With the proliferation of data received from these surveys, the inspection process becomes overwhelming and tedious for multiple use cases (inventory, faults and more). Enterprises must start to realize the value of AI in their operations.
Digital twins allow the customer to manage and inspect its assets properly – whether it’s for tracking installed inventory, monitoring asset optimization, planning maintenance tasks or creating an accurate and available in-house database. All the data is used not only to monitor towers, but also identify new revenue opportunities.
vHive’s digital twin technology allows users additional precise inspection and analysis tools, complemented with behind-the-scene state of the art computer vision and deep learning capabilities, to ensure a full site inspection can be performed.
Object recognition and detection are useful core abilities that play a key role in the process of identifying and differing various types of equipment such as telecom antennas, cranes etc. Today, these kinds of tasks are mostly done via AI methods. Since computer vision AI algorithms are reliable and robust, it is the perfect means for gaining digital twin insights.
These methods are faster, consistent, and constantly improving, bringing better accuracy with less resources. When dealing with big data, as is the case in asset inspection and management, these algorithms bring double the value since they are automatic and are more accurate than relying on a human.
vHive combines both AI and classic computer vision algorithms for maximal and ideal outputs, in both 2D images and 3D models, providing full automation for arduous inspection tasks like inventory and fault detection. vHive delivers cutting-edge entirety of technologies, fused in the best way to give its customers ultimate assets inspection capabilities.
Future computer vision & AI advances will enable automatic detection of additional objects, assets deterioration and defects alerts, spatial exact localization and data acquisition for more complex asset types.