US 12,468,943 B2
System and method for estimating vegetation coverage in a real-world environment
Artzai Picon Ruiz, Derio (ES); Miguel Gonzalez San Emeterio, Derio (ES); Aranzazu Bereciartua-Perez, Derio (ES); Laura Gomez Zamanillo, Derio (ES); Carlos Javier Jimenez Ruiz, Seville (ES); Javier Romero Rodriguez, Seville (ES); Christian Klukas, Limburgerhof (DE); Till Eggers, Ludwigshafen am Rhein (DE); Jone Echazarra Huguet, Derio (ES); and Ramon Navarra-Mestre, Limburgerhof (DE)
Assigned to BASF SE, Ludwigshafen am Rhein (DE)
Appl. No. 17/925,250
Filed by BASF SE, Ludwigshafen am Rhein (DE)
PCT Filed May 7, 2021, PCT No. PCT/EP2021/062178
§ 371(c)(1), (2) Date Nov. 14, 2022,
PCT Pub. No. WO2021/233698, PCT Pub. Date Nov. 25, 2021.
Claims priority of application No. 20176124 (EP), filed on May 22, 2020.
Prior Publication US 2023/0230373 A1, Jul. 20, 2023
Int. Cl. G06N 3/08 (2023.01); G06T 7/12 (2017.01); G06V 10/143 (2022.01); G06V 10/74 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 20/10 (2022.01)
CPC G06N 3/08 (2013.01) [G06T 7/12 (2017.01); G06V 10/143 (2022.01); G06V 10/761 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 20/188 (2022.01); G06T 2207/10024 (2013.01)] 12 Claims
OG exemplary drawing
 
1. A computer-implemented method for estimating vegetation coverage in a real-world environment, the method comprising:
receiving an RGB image of a real-world scenery with one or more plant elements of one or more plant species;
providing at least one channel of the RGB image to a semantic regression neural network, the semantic regression neural network trained to estimate at least a near-infrared channel (NIR) from the at least one channel of the RGB image, the semantic regression neural network having a topology based on a convolutional segmentation neural network with its last layer being substituted by a monotonic activation function and its loss function being substituted by a regression loss function, to learn a pixel-wise regression transformation that transforms any RGB channel from an RGB domain to a target domain comprising at least a near-infrared domain;
obtaining an estimate of the near-infrared channel (NIR) by applying the semantic regression neural network to the RGB image;
deriving at least one infrared-dark channel of a multi-channel image, wherein infrared-dark channel values for each pixel are based on: the value of the near-infrared channel and a minimum value of available R-, G-, B-channels (R, G, B) and the near-infrared channel (NIR) for the respective pixel,
wherein the at least one infrared-dark channel is derived either (i) by applying respective mathematical operations to the estimated near-infrared channel or (ii) by configuring the semantic regression neural network to derive the at least one infrared-dark channel together with the near-infrared channel;
providing the multi-channel image comprising at least one of the R-, G-, B-channels (R, G, B) of the RGB image, and the near-infrared channel (NIR), as test input (TI1) to a semantic segmentation neural network, the semantic segmentation neural network trained with a training data set comprising multi-channel images of the test input type to segment the test input (TI1) into pixels associated with the plant elements and pixels not associated with the plant elements; and
segmenting the test input (TI1) using the semantic segmentation neural network resulting in a vegetation coverage map indicating pixels of the test input associated with the plant elements and indicating pixels of the test input not associated with the plant elements.