Efficient Dense Modules of Asymmetric Convolution for Real-Time Semantic Segmentation

Understand

Real-time semantic segmentation plays an important role in practical applications such as self-driving and robots.

• Most semantic segmentation research focuses on improving estimation accuracy with little consideration on efficiency.
• Several previous studies that emphasize high-speed inference often fail to produce high-accuracy segmentation results.
• In this paper, we propose a novel convolutional network named Efficient Dense modules with Asymmetric convolution (EDANet), which employs an asymmetric convolution structure and incorporates dilated convolution and dense connectivity to achieve high efficiency at low computational cost and model size.