Patent ID: 11900646
Assignee: SIEMENS AKTIENGESELLSCHAFT
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 11:
12. In a non-transitory computer-readable storage medium that stores instructions executable by one or more processors to generate a deep neural net for localizing objects of a predetermined type in an input image, the instructions comprising:
training a discriminative deep counting model to classify images according to a number of objects of the predetermined type depicted in each of the images, wherein the discriminative deep counting model is trained for at least two different classes corresponding to different numbers of objects of the predetermined type;
training a deep segmentation model to segment images, the training of the deep segmentation model comprising classifying each pixel of a respective image according to what part of the respective image the respective pixel belongs to;
forming the deep neural net, the forming of the deep neural net comprising combining parts of the discriminative deep counting model and the segmentation model to form the deep neural net, wherein the deep neural net is structured, such that by processing the respective input image, a respective corresponding map indicating locations of any objects of the predetermined type for each processed input image is generated; and
adding an upsampling module after a final convolutional layer of the deep neural net, wherein the upsampling module comprises an image processing algorithm based neither on machine learning nor on a neural net architecture, wherein the upsampling module is configured to take the generated map as an input and to generate, from the generated map, an upsampled map.