# EADNet: Efficient Asymmetric Dilated Network for Semantic Segmentation Source: [EADNet: Efficient Asymmetric Dilated Network for Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:02:46.333Z Authors: Qihang Yang, Tao Chen, Jiayuan Fan, Ye Lu, Chongyan Zuo, Qinghua Chi Published: 2021-03-16T08:46:57.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()