Semantic Segmentation by Multi-Scale Feature Extraction Based on Grouped Dilated Convolution Module
Existing studies have shown that effective extraction of multi-scale information is a crucial factor directly related to the increase in performance of semantic segmentation. Accordingly, various methods for extracting multi-scale information have been developed. However, these methods face problems...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/9/9/947 |
| _version_ | 1827694251475992576 |
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| author | Dong Seop Kim Yu Hwan Kim Kang Ryoung Park |
| author_facet | Dong Seop Kim Yu Hwan Kim Kang Ryoung Park |
| author_sort | Dong Seop Kim |
| collection | DOAJ |
| description | Existing studies have shown that effective extraction of multi-scale information is a crucial factor directly related to the increase in performance of semantic segmentation. Accordingly, various methods for extracting multi-scale information have been developed. However, these methods face problems in that they require additional calculations and vast computing resources. To address these problems, this study proposes a grouped dilated convolution module that combines existing grouped convolutions and atrous spatial pyramid pooling techniques. The proposed method can learn multi-scale features more simply and effectively than existing methods. Because each convolution group has different dilations in the proposed model, they have receptive fields of different sizes and can learn features corresponding to these receptive fields. As a result, multi-scale context can be easily extracted. Moreover, optimal hyper-parameters are obtained from an in-depth analysis, and excellent segmentation performance is derived. To evaluate the proposed method, open databases of the Cambridge Driving Labeled Video Database (CamVid) and the Stanford Background Dataset (SBD) are utilized. The experimental results indicate that the proposed method shows a mean intersection over union of 73.15% based on the CamVid dataset and 72.81% based on the SBD, thereby exhibiting excellent performance compared to other state-of-the-art methods. |
| first_indexed | 2024-03-10T12:01:30Z |
| format | Article |
| id | doaj.art-0ca14781bbd34d3296836b3752821f8e |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T12:01:30Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-0ca14781bbd34d3296836b3752821f8e2023-11-21T16:51:35ZengMDPI AGMathematics2227-73902021-04-019994710.3390/math9090947Semantic Segmentation by Multi-Scale Feature Extraction Based on Grouped Dilated Convolution ModuleDong Seop Kim0Yu Hwan Kim1Kang Ryoung Park2Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro, 1-gil, Jung-gu, Seoul 04620, KoreaDivision of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro, 1-gil, Jung-gu, Seoul 04620, KoreaDivision of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro, 1-gil, Jung-gu, Seoul 04620, KoreaExisting studies have shown that effective extraction of multi-scale information is a crucial factor directly related to the increase in performance of semantic segmentation. Accordingly, various methods for extracting multi-scale information have been developed. However, these methods face problems in that they require additional calculations and vast computing resources. To address these problems, this study proposes a grouped dilated convolution module that combines existing grouped convolutions and atrous spatial pyramid pooling techniques. The proposed method can learn multi-scale features more simply and effectively than existing methods. Because each convolution group has different dilations in the proposed model, they have receptive fields of different sizes and can learn features corresponding to these receptive fields. As a result, multi-scale context can be easily extracted. Moreover, optimal hyper-parameters are obtained from an in-depth analysis, and excellent segmentation performance is derived. To evaluate the proposed method, open databases of the Cambridge Driving Labeled Video Database (CamVid) and the Stanford Background Dataset (SBD) are utilized. The experimental results indicate that the proposed method shows a mean intersection over union of 73.15% based on the CamVid dataset and 72.81% based on the SBD, thereby exhibiting excellent performance compared to other state-of-the-art methods.https://www.mdpi.com/2227-7390/9/9/947semantic segmentationpixel-level classificationgrouped dilated convolution modulemulti-scale context |
| spellingShingle | Dong Seop Kim Yu Hwan Kim Kang Ryoung Park Semantic Segmentation by Multi-Scale Feature Extraction Based on Grouped Dilated Convolution Module Mathematics semantic segmentation pixel-level classification grouped dilated convolution module multi-scale context |
| title | Semantic Segmentation by Multi-Scale Feature Extraction Based on Grouped Dilated Convolution Module |
| title_full | Semantic Segmentation by Multi-Scale Feature Extraction Based on Grouped Dilated Convolution Module |
| title_fullStr | Semantic Segmentation by Multi-Scale Feature Extraction Based on Grouped Dilated Convolution Module |
| title_full_unstemmed | Semantic Segmentation by Multi-Scale Feature Extraction Based on Grouped Dilated Convolution Module |
| title_short | Semantic Segmentation by Multi-Scale Feature Extraction Based on Grouped Dilated Convolution Module |
| title_sort | semantic segmentation by multi scale feature extraction based on grouped dilated convolution module |
| topic | semantic segmentation pixel-level classification grouped dilated convolution module multi-scale context |
| url | https://www.mdpi.com/2227-7390/9/9/947 |
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