Seechange Technologies Limited

Apical Limited

David Packwood

Vladislav Terekhov

Michael Andrew Pallister

Ariel Edgar Ruiz-garcia

System, apparatus and method of image processing to detect a substance spill on a solid surface such as a floor is disclosed. First data representing a first image, captured by an image sensor, of a region including a solid surface, is received. A trained semantic segmentation neural network is applied to the first image data to determine, for each pixel of the first image, a spill classification value associated with the pixel, the determined spill classification value for a given pixel indicating the extent to which the trained semantic segmentation neural network estimates, based on its training, that the given pixel illustrates a substance spill. The presence of a substance spill on the solid surface is detected based on the determined spill classification values of the pixels of the first image.

Apparatus and method of image processing to detect a substance spill on a solid surface

A computing network has a sensor, a first processor in a first computing network location, and a second processor in a second computing network location, the second computing network location further from the sensor than the first computing network location. The first processor is configured to receive sensor data from the sensor and configured to operate a first machine learning model to make a first inference based on the sensor data. The second processor is configured to receive the sensor data and to operate a second machine learning model to make a second inference based on the sensor data in response to a trigger. The computing network is configured to collate and process the first and second inferences to make an aggregated inference.

Computing networks

There is provided a computer-implemented method of processing object detection data. The method includes receiving, from an object detection system, object detection data comprising a plurality of detection outputs associated with different respective regions of an image, wherein a first detection output of the plurality of detection outputs is associated with a first region of the image and comprises a plurality of received detection characteristics. The method includes processing the first detection output to determine one or more modified detection characteristics of said plurality of received detection characteristics. Processing the first detection output includes retrieving a mapping function and applying the mapping function, where the mapping is dependent upon at least one of the plurality of received detection characteristics.

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Cambridge, United Kingdom

David Packwood