Deep Ensembles Based on Stochastic Activations for Semantic Segmentation
Semantic segmentation is a very popular topic in modern computer vision, and it has applications in many fields. Researchers have proposed a variety of architectures for semantic image segmentation. The most common ones exploit an encoder–decoder structure that aims to capture the semantics of the i...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Signals |
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| Online Access: | https://www.mdpi.com/2624-6120/2/4/47 |
| _version_ | 1827669598254661632 |
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| author | Alessandra Lumini Loris Nanni Gianluca Maguolo |
| author_facet | Alessandra Lumini Loris Nanni Gianluca Maguolo |
| author_sort | Alessandra Lumini |
| collection | DOAJ |
| description | Semantic segmentation is a very popular topic in modern computer vision, and it has applications in many fields. Researchers have proposed a variety of architectures for semantic image segmentation. The most common ones exploit an encoder–decoder structure that aims to capture the semantics of the image and its low-level features. The encoder uses convolutional layers, in general with a stride larger than one, to extract the features, while the decoder recreates the image by upsampling and using skip connections with the first layers. The objective of this study is to propose a method for creating an ensemble of CNNs by enhancing diversity among networks with different activation functions. In this work, we use DeepLabV3+ as an architecture to test the effectiveness of creating an ensemble of networks by randomly changing the activation functions inside the network multiple times. We also use different backbone networks in our DeepLabV3+ to validate our findings. A comprehensive evaluation of the proposed approach is conducted across two different image segmentation problems: the first is from the medical field, i.e., polyp segmentation for early detection of colorectal cancer, and the second is skin detection for several different applications, including face detection, hand gesture recognition, and many others. As to the first problem, we manage to reach a Dice coefficient of 0.888, and a mean intersection over union (mIoU) of 0.825, in the competitive Kvasir-SEG dataset. The high performance of the proposed ensemble is confirmed in skin detection, where the proposed approach is ranked first concerning other state-of-the-art approaches (including HarDNet) in a large set of testing datasets. |
| first_indexed | 2024-03-10T03:05:28Z |
| format | Article |
| id | doaj.art-1d5a56912f0b414f81a3267cf3a4afc4 |
| institution | Directory Open Access Journal |
| issn | 2624-6120 |
| language | English |
| last_indexed | 2024-03-10T03:05:28Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Signals |
| spelling | doaj.art-1d5a56912f0b414f81a3267cf3a4afc42023-11-23T10:33:07ZengMDPI AGSignals2624-61202021-11-012482083310.3390/signals2040047Deep Ensembles Based on Stochastic Activations for Semantic SegmentationAlessandra Lumini0Loris Nanni1Gianluca Maguolo2Department of Computer Science and Engineering, University of Bologna, Via dell’Università 50, 47521 Cesena, ItalyDepartment of Information Engineering, University of Padua, viale Gradenigo 6, 35122 Padua, ItalyDepartment of Information Engineering, University of Padua, viale Gradenigo 6, 35122 Padua, ItalySemantic segmentation is a very popular topic in modern computer vision, and it has applications in many fields. Researchers have proposed a variety of architectures for semantic image segmentation. The most common ones exploit an encoder–decoder structure that aims to capture the semantics of the image and its low-level features. The encoder uses convolutional layers, in general with a stride larger than one, to extract the features, while the decoder recreates the image by upsampling and using skip connections with the first layers. The objective of this study is to propose a method for creating an ensemble of CNNs by enhancing diversity among networks with different activation functions. In this work, we use DeepLabV3+ as an architecture to test the effectiveness of creating an ensemble of networks by randomly changing the activation functions inside the network multiple times. We also use different backbone networks in our DeepLabV3+ to validate our findings. A comprehensive evaluation of the proposed approach is conducted across two different image segmentation problems: the first is from the medical field, i.e., polyp segmentation for early detection of colorectal cancer, and the second is skin detection for several different applications, including face detection, hand gesture recognition, and many others. As to the first problem, we manage to reach a Dice coefficient of 0.888, and a mean intersection over union (mIoU) of 0.825, in the competitive Kvasir-SEG dataset. The high performance of the proposed ensemble is confirmed in skin detection, where the proposed approach is ranked first concerning other state-of-the-art approaches (including HarDNet) in a large set of testing datasets.https://www.mdpi.com/2624-6120/2/4/47semantic segmentationactivation functiondeep ensembles |
| spellingShingle | Alessandra Lumini Loris Nanni Gianluca Maguolo Deep Ensembles Based on Stochastic Activations for Semantic Segmentation Signals semantic segmentation activation function deep ensembles |
| title | Deep Ensembles Based on Stochastic Activations for Semantic Segmentation |
| title_full | Deep Ensembles Based on Stochastic Activations for Semantic Segmentation |
| title_fullStr | Deep Ensembles Based on Stochastic Activations for Semantic Segmentation |
| title_full_unstemmed | Deep Ensembles Based on Stochastic Activations for Semantic Segmentation |
| title_short | Deep Ensembles Based on Stochastic Activations for Semantic Segmentation |
| title_sort | deep ensembles based on stochastic activations for semantic segmentation |
| topic | semantic segmentation activation function deep ensembles |
| url | https://www.mdpi.com/2624-6120/2/4/47 |
| work_keys_str_mv | AT alessandralumini deepensemblesbasedonstochasticactivationsforsemanticsegmentation AT lorisnanni deepensemblesbasedonstochasticactivationsforsemanticsegmentation AT gianlucamaguolo deepensemblesbasedonstochasticactivationsforsemanticsegmentation |