Low- and high-fidelity classifiers applied to road-scene images

A method for object classification and location information detection, comprising:

• down-sampling an image to a down-sampled version of the image;
• wherein down-sampling the image to the down-sampled version of the image comprises calculating a maximum factor by which the image can be down-sampled to generate the down-sampled version while maintaining a ratio of entropy in the down-sampled version to entropy in the image above a predetermined threshold level;
• extracting a set of overlapping zones covering the down-sampled version, as definable by a sliding window with dimensions equal to dimensions of the set of overlapping zones;
• selecting a probable zone from the set of overlapping zones for which a low-fidelity classifier, comprising a first Convolutional Neural Network (CNN), indicates a probability of a presence of an object pertaining to a class of objects classifiable by the low-fidelity classifier;
• mapping the probable zone selected from the down-sampled version to a sector of a higher-resolution version of the image;
• confirming the presence of the object by applying the sector to a high-fidelity classifier, comprising a second CNN, where applying the sector indicates the presence;
• and
• providing a driving assistance to an automated driving system of a vehicle to be executed by the automated driving system based on the presence of the object.