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Autonomous driving is a challenging topic that requires complex solutions inperception tasks such as recognition of road, lanes, traffic signs or lights,vehicles and pedestrians. Through years of research, computer vision has growncapable of tackling these tasks with monocular detectors that can provideremarkable detection rates with relatively low processing times. However, therecent appearance of Convolutional Neural Networks (CNNs) has revolutionizedthe computer vision field and has made possible approaches to perform fullpixel-wise semantic segmentation in times close to real time (even on hardwarethat can be carried on a vehicle). In this paper, we propose to use full imagesegmentation as an approach to simplify and unify most of the detection tasksrequired in the perception module of an autonomous vehicle, analyzing majorconcerns such as computation time and detection performance.

Can we unify monocular detectors for autonomous driving by using the pixel-wise semantic segmentation of CNNs?