  Due to real-time image semantic segmentation needs on power constrained edge
devices, there has been an increasing desire to design lightweight semantic
segmentation neural network, to simultaneously reduce computational cost and
increase inference speed. In this paper, we propose an efficient asymmetric
dilated semantic segmentation network, named EADNet, which consists of multiple
developed asymmetric convolution branches with different dilation rates to
capture the variable shapes and scales information of an image. Specially, a
multi-scale multi-shape receptive field convolution (MMRFC) block with only a
few parameters is designed to capture such information. Experimental results on
the Cityscapes dataset demonstrate that our proposed EADNet achieves
segmentation mIoU of 67.1 with smallest number of parameters (only 0.35M) among
mainstream lightweight semantic segmentation networks.
