# US 11,113,561 B2 · Method, artificial neural network, device, computer program and machine-readable memory medium for the semantic segmentation of image data > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,113,561 B2 - Document ID: US11113561B2 - Status: Granted - Kind: B2 - Application number: 16/590,905 - Filed: 2019-10-02 - Published: 2020-04-09 - Granted: 2021-09-07 - Earliest claimed: 2018-10-05 - Assignees: Robert Bosch GmbH - Applicants: Robert Bosch GmbH - Inventors: Ferran Diego Andilla; Dimitrios Bariamis; Masato Takami; Uwe Brosch - Web page: https://patentgrep.com/patent/US11113561B2 ## CPC - G06N 3/08: Learning methods - G06F 18/2451: linear, e.g. hyperplane - G06N 3/045: Combinations of networks - G06N 3/0455: Auto-encoder networks; Encoder-decoder networks - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06V 10/764: using classification, e.g. of video objects - G06V 10/82: using neural networks - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract Method for the calculation resource-saving semantic segmentation of image data of an imaging sensor with the aid of an artificial neural network, in particular, of a convolutional neural network, the artificial neural network including an encoder path, a decoder path, the encoder path transitioning into the decoder path, the transition taking place via a discriminative path, the following steps taking place in the discriminative path: dividing an input tensor as a function of a division function into at least one first slice tensor and at least one second slice tensor, the input tensor originating from the encoder path; connecting the at least one first slice tensor to the at least one second slice tensor as a function of a connection function in order to obtain a class tensor; and outputting the class tensor to the decoder path of the neural network. ## Claims ### Claim 1 A method for providing calculation resource-saving semantic segmentation of image data of an imaging sensor with an artificial neural network, the method comprising: performing the following in a discriminative path of the artificial neural network, which includes an encoder path, a decoder path, the encoder path transitioning into the decoder path, the transition taking place via the discriminative path: dividing an input tensor as a function of a division function into at least one first slice tensor and at least one second slice tensor, the input tensor originating from the encoder path; connecting the at least one first slice tensor to the at least one second slice tensor as a function of a first connection function to obtain at least one concatenated tensor; connecting the at least one first slice tensor to a class tensor as a function of a second connection function to obtain a decoder tensor, the class tensor being of the at least one concatenated tensor; and outputting the decoder tensor to the decoder path of the neural network. ### Claim 2 (depends on 1) The method of claim 1, wherein the division function is configured so that only a subset of the feature maps of the input tensor is selected for forming the at least one first slice tensor. ### Claim 3 (depends on 1) The method of claim 1, wherein the first connection function and/or the second connection function is configured so that the dimension of the input tensor is maintained. ### Claim 4 (depends on 1) The method of claim 1, further comprising: receiving the input tensor and the division function. ### Claim 5 (depends on 1) The method of claim 1, wherein in the dividing, a first function of a neural network is applied to the at least one first slice tensor and a second function of a neural network is applied to the at least one second slice tensor. ### Claim 6 (depends on 5) The method of claim 5, wherein the division function is configured so that it includes the number of feature maps to be calculated and the respective functions of an artificial neural network for calculating the at least one first slice tensor and the at least one second slice tensor. ### Claim 7 (depends on 1) The method of claim 1, wherein the concatenated tensor is configured to form a class tensor by applying functions of an artificial neural network. ### Claim 8 A network for semantically segmenting image data of an imaging sensor, comprising: an artificial neural network, including an encoder path, a decoder path, the encoder path transitioning into the decoder path, the transition taking place via a discriminative path, configured to perform the following in the discriminative path: dividing an input tensor as a function of a division function into at least one first slice tensor and at least one second slice tensor, the input tensor originating from the encoder path; connecting the at least one first slice tensor to the at least one second slice tensor as a function of a first connection function to obtain at least one concatenated tensor; connecting the at least one first slice tensor to a class tensor as a function of a second connection function to obtain a decoder tensor, the class tensor being of the at least one concatenated tensor; and outputting the decoder tensor to the decoder path of the neural network. ### Claim 9 A device for semantically segmenting image data of an imaging sensor, comprising: an artificial neural network, including an encoder path, a decoder path, the encoder path transitioning into the decoder path, the transition taking place via a discriminative path, configured to perform the following in the discriminative path: dividing an input tensor as a function of a division function into at least one first slice tensor and at least one second slice tensor, the input tensor originating from the encoder path; connecting the at least one first slice tensor to the at least one second slice tensor as a function of a first connection function to obtain at least one concatenated tensor; connecting the at least one first slice tensor to a class tensor as a function of a second connection function to obtain a decoder tensor, the class tensor being of the at least one concatenated tensor; and outputting the decoder tensor to the decoder path of the neural network. ### Claim 10 A non-transitory computer readable medium having a computer program, which is executable by a processor, comprising: a program code arrangement having program code for providing calculation resource-saving semantic segmentation of image data of an imaging sensor with an artificial neural network, by performing the following in a discriminative path of the artificial neural network, which includes an encoder path, a decoder path, the encoder path transitioning into the decoder path, the transition taking place via the discriminative path: dividing an input tensor as a function of a division function into at least one first slice tensor and at least one second slice tensor, the input tensor originating from the encoder path; connecting the at least one first slice tensor to the at least one second slice tensor as a function of a first connection function to obtain at least one concatenated tensor; connecting the at least one first slice tensor to a class tensor as a function of a second connection function to obtain a decoder tensor, the class tensor being of the at least one concatenated tensor; and outputting the decoder tensor to the decoder path of the neural network. ### Claim 11 (depends on 10) The computer readable medium of claim 10, wherein the division function is configured so that only a subset of the feature maps of the input tensor is selected for forming the at least one first slice tensor. ### Claim 12 (depends on 10) The computer readable medium of claim 10, wherein the artificial neural network includes a convolutional neural network. ### Claim 13 (depends on 1) The artificial neural network of claim 1, wherein the artificial neural network includes a convolutional neural network. ### Claim 14 (depends on 8) The network of claim 8, wherein the artificial neural network includes a convolutional neural network. ### Claim 15 (depends on 9) The device of claim 9, wherein the artificial neural network includes a convolutional neural network.