Machine Learning Surface Measurement via Existing Reference Objects

The system automatically detects and utilizes reference objects within the captured image without requiring the user to manually place or position a reference object. The machine learning engine identifies standardized reference objects (such as light switches, outlets, or other objects with known dimensions) that are already present in the scene, enabling the system to self-determine measurement scale without user intervention for reference object placement.

The patent introduces a machine learning engine as an intermediary between the camera and the measurement system. This engine processes the captured image, detects reference objects, determines their actual dimensions, and calculates the scale factor, thereby mediating the complex task of accurate measurement without requiring direct user involvement in reference object management.

The machine learning engine serves multiple functions within the system: it detects reference objects, determines their actual dimensions, calculates scale factors, and enables surface dimension measurements. By consolidating these multiple functions into a single multi-functional component, the patent reduces overall system complexity compared to having separate modules for each function.

The system changes the parameter approach from requiring physical reference objects to be manually positioned to using machine learning-based detection of pre-existing reference objects. This parameter change in the detection method enables accurate measurement while simplifying the user interaction process, though it does increase computational complexity which is managed through efficient algorithms.