Abstract: A method for assessing the reliability of a prediction made by a deep neural network includes inputting input data to a trained deep neural network, determining a class probability score of the detection, wherein the class probability score is determined by fitting of at least two multiple outlier detection methods, determining whether the input data is or is not an outlier, comparing the class probability score to a threshold value, and outputting that the prediction is or is not reliable. Also disclosed is a system and a computer readable storage medium.

Abstract: An inspection device and a method for checking an object produced by sintering for potential defects, wherein if at least one predefined prerequisite is satisfied, at least one image is recorded of at least part of the object via an imaging device in the inspection device, and it is checked by the inspection device whether a defect can be detected in the object in at least one image. A predication relating to a potential defect in the object is provided to the inspection device prior to recording the at least one image, and at least one imaging parameter is determined on the basis of the prediction, wherein the at least one image is recorded on the basis of the at least one determined imaging parameter.

Abstract: The disclosure relates to a method for pedestrian detection in a processor circuit of a motor vehicle, wherein an image data set describing an image of an environment of the motor vehicle is received from an environment sensor, and a machine learning model (ML model) is used to determine bounding boxes with potential images of pedestrians using the image data set, and from image data of the image data set the at least one ML model extracts feature data of image features and a detection of a completely or partially depicted pedestrian is carried out within a bounding box using the image features contained therein by way of a classifier of the at least one ML model, and the bounding box depicting the pedestrian is identified by a detection signal as the result of the detection.

Abstract: The present invention relates to a method, a computer program, and an apparatus for training a neural network for semantic image segmentation. The invention further relates to an in-car control unit or a backend system, which make use of such a method or apparatus, and to a vehicle comprising such an in-car control unit. In some embodiments and in a first step, image data of a sequence of image frames are received. Then a frame-based evaluation of semantic segmentation predictions of one or more objects in individual image frames is performed. Furthermore, a sequence-based evaluation of temporal characteristics of semantic segmentation predictions of said one or more objects in at least two image frames is performed. The results of the frame-based evaluation and the sequence-based evaluation are combined.

Abstract: Method for automatically executing a vehicle function of a vehicle based on spatially resolved raw sensor data for environment perception generated by at least one sensor for environment detection of the vehicle, comprising: receiving spatially resolved raw sensor data generated by the at least one sensor of the vehicle; processing sensor data which are characteristic of the spatially resolved raw sensor data by a processor, the processor determining at least one region of interest of the sensor data and at least one class for classifying the region of interest; processing the sensor data by an evaluation circuit based on the determined region of interest and hereby determining at least one local quality parameter which is characteristic for the quality of the sensor data with respect to at least a section of the region of interest; executing the vehicle function in dependence on the local quality parameter.

Abstract: An inspection device and a method for checking an object produced by sintering for potential defects, wherein if at least one predefined prerequisite is satisfied, at least one image is recorded of at least part of the object via an imaging device in the inspection device, and it is checked by the inspection device whether a defect can be detected in the object in at least one image. A predication relating to a potential defect in the object is provided to the inspection device prior to recording the at least one image, and at least one imaging parameter is determined on the basis of the prediction, wherein the at least one image is recorded on the basis of the at least one determined imaging parameter.