Papers › Efficient Dense Modules of Asymmetric Convolution for Real-Time Semantic Segmentation
Efficient Dense Modules of Asymmetric Convolution for Real-Time Semantic Segmentation
Shao-Yuan Lo, Hsueh-Ming Hang, Sheng-Wei Chan, Jing-Jhih Lin
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. EDANet is 2.7 times faster than the existing fast segmentation network, ICNet, while it achieves a similar mIoU score without any additional context module, post-processing scheme, and pretrained model. We evaluate EDANet on Cityscapes and CamVid datasets, and compare it with the other state-of-art systems. Our network can run with the high-resolution inputs at the speed of 108 FPS on one GTX 1080Ti.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Real-Time Semantic Segmentation | CamVid | EDANet | mIoU | 66.4 | #24 of 29 | Archive leaderboard | report |
| Real-Time Semantic Segmentation | Cityscapes test | EDANet | Frame (fps) | 108.7 (1080Ti) | #35 of 39 | Archive leaderboard | report |
| Real-Time Semantic Segmentation | Cityscapes test | EDANet | Time (ms) | 9.2 | #35 of 39 | Archive leaderboard | report |
| Real-Time Semantic Segmentation | Cityscapes test | EDANet | mIoU | 67.3 | #35 of 39 | Archive leaderboard | report |
| Semantic Segmentation | CamVid | EDANet | Global Accuracy | 90.8 | #13 of 21 | Archive leaderboard | report |
| Semantic Segmentation | CamVid | EDANet | Mean IoU | 66.4 | #13 of 21 | Archive leaderboard | report |
| Semantic Segmentation | Cityscapes test | EDANet | Mean IoU (class) | 67.3 | #89 of 105 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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