1. A method, comprising: receiving image data for one or more images; processing the image data and generating therefrom location and classification information of objects within the one or more images by a Convolutional Neural Network (CNN), the generating including: processing the image data by an initial set of multiple layers including convolutional, pooling and rectified linear unit (ReLU) layers; processing output of the initial set of layers by one or more convolutional layers configured to act as scaling layers; receiving output of the scaling layers at a Region Of Interest (ROI) selector and generating therefrom a list of one or more object location proposals within the one or more images; receiving the generated list at a pooling layer and extracting therefrom a corresponding feature representation for one or more of the object location proposals; analyzing the feature representations and corresponding object location proposals by a first set of one or more fully connected layers and, based on said analyzing, constructing a feature vector; from the feature vector determining by a second set of one or more fully connected layers a probability of objects in the corresponding object location proposals belonging to one of a plurality of object categories; and from the feature vector determining by a third set of one or more fully connected layers of whether to adjust the corresponding object location proposals for the objects, wherein the second and third sets of fully connected layers are independent.