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.

Abstract: A floating platform for high-power wind turbines, comprising a concrete substructure, said concrete substructure forming the base of the platform, which remains semi-submerged in the operating position, and consisting of a square lower slab on which a series of beams and five hollow reinforced concrete cylinders are constructed, distributed at the corners and the center of said lower slab; a metal superstructure supported on the concrete substructure and forming the base for connection with the wind turbine tower, said tower being coupled at the center thereof; and metal covers covering each of the cylinders, on which the metal superstructure is supported and to which vertical pillars are secured, linked together by beams, which join at the central pillar by an element whereon the base of the wind turbine tower is secured.

Abstract: Quantifying plant infestation is performed by estimating the number of biological objects (132) on parts (122) of a plant (112). A computer (202) receives a plant-image (412) taken from a particular plant (112). The computer (202) uses a first convolutional neural network (262/272) to derive a part-image (422) that shows a part of the plant. The computer (202) splits the part-image into tiles and uses a second network to process the tiles to density maps. The computer (202) combines the density maps to a combined density map in the dimension of the part-image and integrates the pixel values to an estimate number of objects for the part. Object classes (132(1), 132(2)) can be differentiated to fine-tune the quantification to identify class-specific countermeasures.

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.

Abstract: A computer-implemented method, computer program product and computer system (100) for determining the impact of herbicides on crop plants (11) in an agricultural field (10). The system includes an interface (110) to receive an image (20) with at least one crop plant representing a real world situation in the agricultural field (10) after herbicide application. An image pre-processing module (120) rescales the received image (20) to a rescaled image (20a) matching the size of an input layer of a first fully convolutional neural network (CNN1) referred to as the first CNN. The first CNN is trained to segment the rescaled image (20a) into crop (11) and non-crop (12, 13) portions, and provides a first segmented output (20s1) indicating the crop portions (20c) of the rescaled image with pixels belonging to representations of crop.

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).