Papers › DenseASPP for Semantic Segmentation in Street Scenes
DenseASPP for Semantic Segmentation in Street Scenes
Maoke Yang, Kun Yu, Chi Zhang, Zhiwei Li, Kuiyuan Yang
Semantic image segmentation is a basic street scene understanding task in autonomous driving, where each pixel in a high resolution image is categorized into a set of semantic labels. Unlike other scenarios, objects in autonomous driving scene exhibit very large scale changes, which poses great challenges for high-level feature representation in a sense that multi-scale information must be correctly encoded. To remedy this problem, atrous convolutioncite{Deeplabv1} was introduced to generate features with larger receptive fields without sacrificing spatial resolution. Built upon atrous convolution, Atrous Spatial Pyramid Pooling (ASPP)cite{Deeplabv2} was proposed to concatenate multiple atrous-convolved features using different dilation rates into a final feature representation. Although ASPP is able to generate multi-scale features, we argue the feature resolution in the scale-axis is not dense enough for the autonomous driving scenario. To this end, we propose Densely connected Atrous Spatial Pyramid Pooling (DenseASPP), which connects a set of atrous convolutional layers in a dense way, such that it generates multi-scale features that not only cover a larger scale range, but also cover that scale range densely, without significantly increasing the model size. We evaluate DenseASPP on the street scene benchmark Cityscapescite{Cityscapes} and achieve state-of-the-art performance.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semantic Segmentation | Cityscapes test | DenseASPP (DenseNet-161) | Mean IoU (class) | 80.6% | #49 of 105 | Archive leaderboard | report |
| Semantic Segmentation | SkyScapes-Dense | DenseASPP (ResNet-101) | Mean IoU | 24.73 | #5 of 7 | Archive leaderboard | report |
| Semantic Segmentation | Trans10K | DenseASPP | GFLOPs | 36.20 | #10 of 15 | Archive leaderboard | report |
| Semantic Segmentation | Trans10K | DenseASPP | mIoU | 63.01% | #10 of 15 | 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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