Can we unify monocular detectors for autonomous driving by using the pixel-wise semantic segmentation of CNNs?
This addresses the need for efficient and unified perception modules in autonomous vehicles, though it appears incremental by applying existing segmentation methods to a known domain.
The paper tackles the problem of unifying multiple detection tasks in autonomous driving perception by using pixel-wise semantic segmentation from CNNs, aiming to simplify the system while analyzing computation time and detection performance.
Autonomous driving is a challenging topic that requires complex solutions in perception tasks such as recognition of road, lanes, traffic signs or lights, vehicles and pedestrians. Through years of research, computer vision has grown capable of tackling these tasks with monocular detectors that can provide remarkable detection rates with relatively low processing times. However, the recent appearance of Convolutional Neural Networks (CNNs) has revolutionized the computer vision field and has made possible approaches to perform full pixel-wise semantic segmentation in times close to real time (even on hardware that can be carried on a vehicle). In this paper, we propose to use full image segmentation as an approach to simplify and unify most of the detection tasks required in the perception module of an autonomous vehicle, analyzing major concerns such as computation time and detection performance.