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Efficient and robust deep networks for semantic segmentation
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DOI：10.1177/0278364917710542.png)
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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.
Keyword:
Semantic segmentation
up-convolutional networks
human body part segmentation
road/lane segmentation
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