DenseASPP for Semantic Segmentation in Street Scenes
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 chal...
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| Veröffentlicht in: | 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition S. 3684 - 3692 |
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IEEE
01.06.2018
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| ISSN: | 1063-6919 |
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| Abstract | 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 convolution[14]was introduced to generate features with larger receptive fields without sacrificing spatial resolution. Built upon atrous convolution, Atrous Spatial Pyramid Pooling (ASPP)[2] 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 Cityscapes[4] and achieve state-of-the-art performance. |
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| AbstractList | 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 convolution[14]was introduced to generate features with larger receptive fields without sacrificing spatial resolution. Built upon atrous convolution, Atrous Spatial Pyramid Pooling (ASPP)[2] 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 Cityscapes[4] and achieve state-of-the-art performance. |
| Author | Li, Zhiwei Yu, Kun Zhang, Chi Yang, Kuiyuan Yang, Maoke |
| Author_xml | – sequence: 1 givenname: Maoke surname: Yang fullname: Yang, Maoke – sequence: 2 givenname: Kun surname: Yu fullname: Yu, Kun – sequence: 3 givenname: Chi surname: Zhang fullname: Zhang, Chi – sequence: 4 givenname: Zhiwei surname: Li fullname: Li, Zhiwei – sequence: 5 givenname: Kuiyuan surname: Yang fullname: Yang, Kuiyuan |
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| Snippet | 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... |
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| SubjectTerms | Autonomous vehicles Convolution Image resolution Image segmentation Kernel Neurons Semantics |
| Title | DenseASPP for Semantic Segmentation in Street Scenes |
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