Device for classifying data

Abstract

A device is configured to classify data. Its operation involves providing () data samples including one or more of: image data, radar data, acoustic data, and/or lidar data to a processing unit. The data samples include at least one test sample, including positive samples and negative samples. Each positive sample has been determined to contain data relating to at least one object to be detected including at least one pedestrian, car, vehicle, truck or bicycle. Each negative sample has been determined not to contain data relating to the at least one object to be detected. These determinations regarding the positive samples and the negative samples are provided as input data, validated by a human operator, and/or provided by the device itself through a learning algorithm. A first plurality of groups is generated () by the processing unit implementing an artificial neural network, wherein at least some of the first plurality of groups are assigned a weighting factor. Each group of the first plurality of groups is populated () by the processing unit implementing the artificial neural network with a different at least one of the plurality of negative samples based on a different feature of sample data similarity for each group, which involves processing the negative samples to determine a number of different features of sample data similarity in order to populate different groups with negative samples that share or substantially share that or a similar feature, wherein at least one of the groups of the first plurality of groups contains at least two negative samples. It is determined () by the processing unit implementing the artificial neural network whether the at least one test sample contains data relating to the at least one object based on the plurality of the positive samples and the first plurality of groups. The artificial neural network implements the learning algorithm.