Abstract :Urban objects are characterized by a very variable representation in terms of shape, texture and color. In addition, they are present multiple times on the images to be analyzed and can be stuck to each other. To carry out the automatic localization and recognition of the different objects we propose to use supervised learning approaches. Due to their characteristics, urban objects are difficult to detect and conventional detection approaches do not offer satisfactory performance. We proposed the use of a wide margin separator network (SVM) in order to better merge the information from the different resolutions and therefore to improve the representativeness of the urban object. The use of an SVM network makes it possible to improve performance but at a significant computational cost. We then proposed to use an activation path making it possible to reduce complexity without losing efficiency. This path will activate the network sequentially and stop the exploration when the probability of detecting an object is high. In the case of a location based on the extraction of characteristics then the classification, the computational reduction is a factor of five. Subsequently, we have shown that we can combine the SVM network with feature maps from convolutional neural networks.