Feature Pyramid Encoding Network for Real-time Semantic Segmentation

Understand

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.