Feature Pyramid Encoding Network for Real-time Semantic Segmentation

Abstract

Although current deep learning methods have achieved impressive results for semantic segmentation, they incur high computational costs and have a huge number of parameters. For real-time applications, inference speed and memory usage are two important factors. To address the challenge, we propose a lightweight feature pyramid encoding network (FPENet) to make a good trade-off between accuracy and speed. Specifically, we use a feature pyramid encoding block to encode multi-scale contextual features with depthwise dilated convolutions in all stages of the encoder. A mutual embedding upsample module is introduced in the decoder to aggregate the high-level semantic features and low-level spatial details efficiently. The proposed network outperforms existing real-time methods with fewer parameters and improved inference speed on the Cityscapes and CamVid benchmark datasets. Specifically, FPENet achieves 68.0\% mean IoU on the Cityscapes test set with only 0.4M parameters and 102 FPS speed on an NVIDIA TITAN V GPU.

arXiv commentAccepted to BMVC 2019

References in corpus (7)

• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
• Res2Net: A New Multi-scale Backbone Architecture
• Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
• ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation
• Pyramid Attention Network for Semantic Segmentation
• ContextNet: Exploring Context and Detail for Semantic Segmentation in Real-time
• CGNet: A Light-weight Context Guided Network for Semantic Segmentation

Cited by in corpus (4)

• nnFormer: Interleaved Transformer for Volumetric Segmentation
• Segmenting Transparent Object in the Wild with Transformer
• Segmenting Transparent Objects in the Wild
• Dense Dual-Path Network for Real-time Semantic Segmentation