Efficient and robust deep networks for semantic segmentation

Abstract

This paper explores and investigates deep convolutional neural network architectures to increase the efficiency and robustness of semantic segmentation tasks. The proposed solutions are based on up-convolutional networks. We introduce three different architectures in this work. The first architecture, called Part-Net, is designed to tackle the specific problem of human body part segmentation and to provide robustness to overfitting and body part occlusion. The second network, called Fast-Net, is a network specifically designed to provide the smallest computation load without losing representation power. Such an architecture is capable of being run on mobile GPUs. The last architecture, called M-Net, aims to maximize the robustness characteristics of deep semantic segmentation approaches through multiresolution fusion. The networks achieve state-of-the-art performance on the PASCAL Parts dataset and competitive results on the KITTI dataset for road and lane segmentation. Moreover, we introduce a new part segmentation dataset, the Freiburg City dataset, which is designed to bring semantic segmentation to highly realistic robotics scenarios. Additionally, we present results obtained with a ground robot and an unmanned aerial vehicle and a full system to explore the capabilities of human body part segmentation in the context of human–robot interaction.