公开(公告)号：US20240095911A1

公开(公告)日：2024-03-21

申请号：US18284484

申请日：2022-03-31

Applicant: BASF SE

Inventor： Rahul TANEJA ,  Kamran SIAL ,  Till EGGERS ,  Margret KEUPER ,  Ramon NAVARRA-MESTRE ,  Sebastian FISCHER ,  Mike SCHARNER ,  Javier ROMERO RODRIGUEZ ,  Francisco Manuel POLO LOPEZ ,  Andres MARTIN PALMA

IPC: G06T7/00 ,  A01G7/00 ,  G06T7/62 ,  G06V10/764 ,  G06V20/10

CPC classification number: G06T7/0012 ,  A01G7/00 ,  G06T7/0004 ,  G06T7/62 ,  G06V10/764 ,  G06V20/188 ,  G06T2200/24 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30004 ,  G06T2207/30188

Abstract: The present disclosure relates to image processing or computer vision techniques. A computer-implemented method is provided for determining a damage status of a physical object, the method comprising the steps of receiving a surface image of the physical object; and providing a pre-trained machine learning model to derive property values from the received surface map, wherein each property value is indicative of a damage index at a respective location, wherein the property values are preferably usable for monitoring and/or controlling a production process of the physical object. In this way, it is possible to reliably identify local defects and ensure that it is accurate enough to apply the chemical products in suitable amounts.

公开(公告)号：US20230230373A1

公开(公告)日：2023-07-20

申请号：US17925250

申请日：2021-05-07

Applicant: BASF SE

Inventor： Artzai PICON RUIZ ,  Miguel GONZALEZ SAN EMETERIO ,  Aranzazu BERECIARTUA-PEREZ ,  Laura GOMEZ ZAMANILLO ,  Carlos Javier JIMENEZ RUIZ ,  Javier ROMERO RODRIGUEZ ,  Christian KLUKAS ,  Till EGGERS ,  Jone ECHAZARRA HUGUET ,  Ramon NAVARRA-MESTRE

IPC: G06V20/10 ,  G06T7/12 ,  G06V10/74 ,  G06V10/764 ,  G06V10/82

CPC classification number: G06V20/188 ,  G06T7/12 ,  G06V10/761 ,  G06V10/764 ,  G06V10/82 ,  G06T2207/10024

Abstract: Computer-implemented method and system (100) for estimating vegetation coverage in a real-world environment. The system receives an RGB image (91) of a real-world scenery (1) with one or more plant elements (10) of one or more plant species. At least one channel of the RGB image (91) is provided to a semantic regression neural network (120) which is trained to estimate at least a near-infrared channel (NIR) from the RGB image. The system obtains an estimate of the near-infrared channel (NIR) by applying the semantic regression neural network (120) to the at least one RGB channel (91). A multi-channel image (92) comprising at least one of the R-, G-, B-channels (R, G, B) of the RGB image and the estimated near-infrared channel (NIR), is provided as test input (TI1) to a semantic segmentation neural network (130) trained with multi-channel images to segment the test input (TI1) into pixels associated with plant elements and pixels not associated with plant elements. The system segments the test input (TI1) using the semantic segmentation neural network (130) resulting in a vegetation coverage map (93) indicating pixels of the test input associated with plant elements (10) and indicating pixels of the test input not associated with plant elements.

公开(公告)号：US20220327815A1

公开(公告)日：2022-10-13

申请号：US17640742

申请日：2020-09-03

Applicant: BASF SE

Inventor： Artzai PICON RUIZ ,  Miguel LINARES DE LA PUERTA ,  Christian KLUKAS ,  Till EGGERS ,  Rainer OBERST ,  Juan Manuel CONTRERAS GALLARDO ,  Javier ROMERO RODRIGUEZ ,  Hikal Khairy Shohdy GAD ,  Gerd KRAEMER ,  Jone ECHAZARRA HUGUET ,  Ramon NAVARRA-MESTRE ,  Miguel GONZALEZ SAN EMETERIO

IPC: G06V10/82 ,  G06V20/10 ,  G06T7/00

Abstract: A computer-implemented method, computer program product and computer system (100) for identifying weeds in a crop field using a dual task convolutional neural network (120) having a topology with an intermediate module (121) to execute a classification task being associated with a first loss function (LF1), and with a semantic segmentation module (122) to execute a segmentation task with a second different loss function (LF2). The intermediate module and the segmentation module are being trained together, taking into account the first and second loss functions (LF1, LF2). The system executes a method including receiving a test input (91) comprising an image showing crop plants of a crop species in an agricultural field and showing weed plants of one or more weed species among said crop plants; predicting the presence of one or more weed species (11, 12, 13) which are present in the respective tile; outputting a corresponding intermediate feature map to the segmentation module as output of the classification task; generating a mask for each weed species class as segmentation output of the second task by extracting multiscale features and context information from the intermediate feature map and concatenating the extracted information to perform semantic segmentation; and generating a final image (92) indicating for each pixel if it belongs to a particular weed species, and if so, to which weed species it belongs.