Enhancing Semantic Segmentation: Design and Analysis of Improved U-Net Based Deep Convolutional Neural Networks Article Swipe
Akash Saxena
,
Prashant Johri
,
Winner Chukwuemeka Ihechiluru
,
Vivek Sharma
,
Megha Rathore
,
Dharmendra Kumar Yadav
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.17762/ijritcc.v12i1.7911
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.17762/ijritcc.v12i1.7911
In this research, we provide a state-of-the-art method for semantic segmentation that makes use of a modified version of the U-Net architecture, which is itself based on deep convolutional neural networks (CNNs). This research delves into the ins and outs of this cutting-edge approach to semantic segmentation in an effort to boost its precision and productivity. To perform semantic segmentation, a crucial operation in computer vision, each pixel in an image must be assigned to one of many predefined item classes. The proposed Improved U-Net architecture makes use of deep CNNs to efficiently capture complex spatial characteristics while preserving associated context. The study illustrates the efficacy of the Improved U-Net in a variety of real-world circumstances through thorough experimentation and assessment. Intricate feature extraction, down-sampling, and up-sampling are all part of the network's design in order to produce high-quality segmentation results. The study demonstrates comparative evaluations against classic U-Net and other state-of-the-art models and emphasizes the significance of hyperparameter fine-tuning. The suggested architecture shows excellent performance in terms of accuracy and generalization, demonstrating its promise for a variety of applications. Finally, the problem of semantic segmentation is addressed in a novel way. The experimental findings validate the relevance of the architecture's design decisions and demonstrate its potential to boost computer vision by enhancing segmentation precision and efficiency.
Related Topics
Concepts
Computer science
Segmentation
Convolutional neural network
Artificial intelligence
Context (archaeology)
Variety (cybernetics)
Feature (linguistics)
Machine learning
Hyperparameter
Generalization
Pattern recognition (psychology)
Philosophy
Linguistics
Biology
Mathematics
Paleontology
Mathematical analysis
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.17762/ijritcc.v12i1.7911
- https://ijritcc.org/index.php/ijritcc/article/download/7911/6443
- OA Status
- diamond
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386968021
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4386968021Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.17762/ijritcc.v12i1.7911Digital Object Identifier
- Title
-
Enhancing Semantic Segmentation: Design and Analysis of Improved U-Net Based Deep Convolutional Neural NetworksWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-09-21Full publication date if available
- Authors
-
Akash Saxena, Prashant Johri, Winner Chukwuemeka Ihechiluru, Vivek Sharma, Megha Rathore, Dharmendra Kumar YadavList of authors in order
- Landing page
-
https://doi.org/10.17762/ijritcc.v12i1.7911Publisher landing page
- PDF URL
-
https://ijritcc.org/index.php/ijritcc/article/download/7911/6443Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://ijritcc.org/index.php/ijritcc/article/download/7911/6443Direct OA link when available
- Concepts
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Computer science, Segmentation, Convolutional neural network, Artificial intelligence, Context (archaeology), Variety (cybernetics), Feature (linguistics), Machine learning, Hyperparameter, Generalization, Pattern recognition (psychology), Philosophy, Linguistics, Biology, Mathematics, Paleontology, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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35Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W3121813218, https://openalex.org/W3104522642, https://openalex.org/W2888780969, https://openalex.org/W3004588635, https://openalex.org/W2784014154, https://openalex.org/W2754736524, https://openalex.org/W2621887673, https://openalex.org/W2754120702, https://openalex.org/W2520594445, https://openalex.org/W2965265842, https://openalex.org/W2292153256, https://openalex.org/W2545031104, https://openalex.org/W4225159693, https://openalex.org/W6814879321, https://openalex.org/W6789348778, https://openalex.org/W2964536579, https://openalex.org/W2972866770, https://openalex.org/W2910554758, https://openalex.org/W4205735790, https://openalex.org/W2973004327, https://openalex.org/W2909335916, https://openalex.org/W2919647648, https://openalex.org/W2921099864, https://openalex.org/W2963136578, https://openalex.org/W1808966389, https://openalex.org/W2799406003, https://openalex.org/W4300860834, https://openalex.org/W2974742461, https://openalex.org/W2792834173, https://openalex.org/W2985735500, https://openalex.org/W4288078255, https://openalex.org/W2936997284, https://openalex.org/W3123941068, https://openalex.org/W3080514906, https://openalex.org/W2780202401 |
| referenced_works_count | 35 |
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