Automation of Fire Door Surveys Using LiDAR and Point Cloud Classification

LiDAR (Light Detection and Ranging) is an advanced technology that uses light in the form of a pulsed laser to measure ranges (variable distances) to objects. By scanning the indoor environment, LiDAR can generate a high-resolution three-dimensional point cloud, a set of data points in space that represents the scanned environment.

Fire door surveys can be automated through the application of machine learning algorithms to classify these point cloud objects or using images. This involves training an algorithm to recognize the specific characteristics of fire doors – such as their size, shape, and location in relation to other objects. Once the algorithm has been trained, it can then automatically identify fire doors in the point cloud data generated by a LiDAR scan.

This automation process eliminates the need for manual surveys, which can be time-consuming and prone to human error. It also enables a more comprehensive and accurate assessment of fire doors, including their number, locations, and potential deficiencies, which are critical for fire safety compliance.

Processing Fire Doors and Fire Signage Using Image Recognition

In addition to LiDAR, image recognition technology can further enhance the automation of fire door surveys. By using high-resolution cameras in conjunction with LiDAR scanners, detailed images of the surveyed environment can be captured. These images can then be processed using image recognition algorithms to identify specific features such as fire doors and fire signage.

Image recognition algorithms are trained on large datasets of images, learning to recognize patterns and features that define different objects. In the context of fire safety surveys, these algorithms can be trained to recognize fire doors and signage based on their distinctive characteristics. For example, fire doors typically have specific features such as intumescent seals and self-closing devices, while fire signage is usually highly distinctive in its colors, shapes, and symbols.