An improved multi-object instance segmentation based on deep learning
Given the recent breakthroughs in recent years, Deep learning (DL) networks have attracted growing interest and attention by researchers and scholars alike due to its importance in detecting and instance segmentation of objects in an image. An entity's example instance segmentation is a critic...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-03-01
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| Series: | Kuwait Journal of Science |
| Online Access: | https://journalskuwait.org/kjs/index.php/KJS/article/view/10879 |
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| author | Nawaf Alshdaifat Mohd Azam Osman Abdullah Zawawi Talib |
| author_facet | Nawaf Alshdaifat Mohd Azam Osman Abdullah Zawawi Talib |
| author_sort | Nawaf Alshdaifat |
| collection | DOAJ |
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Given the recent breakthroughs in recent years, Deep learning (DL) networks have attracted growing interest and attention by researchers and scholars alike due to its importance in detecting and instance segmentation of objects in an image. An entity's example instance segmentation is a critical problem that requires further analysis. However, given the difficulties in adopting object detection and the instance segmentation approach, this study aims to develop an approach to overcome these issues by proposing a new approach based on the recent DL approach in addition to developing an approach for object instance segmentation. The approach presented in this study consisted of three stages in order to improve the recognition approach. First, adopting a DL approach improves the object's detection in the enhanced ResNet (residual neural network) and connects it with the convolution layer for each ResNet block. Second, improving the localization of multiple objects dependent on the Region Proposal Network (RPN) approach, and third, utilizing a complex instance segmentation approach. This study's findings revealed that the suggested approach using a typical benchmark-image dataset, called the COCO dataset, the experiments are carried out and validated using generic evaluation parameters. The proposed approach's performance is verified and measured against the recent image segmentation approach using object instances. The findings also revealed that in terms of average precision over IoU (AP) threshold measurements using different thresholds, the proposed approach obtained improved results compared to other well-known segmentation approaches.
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| format | Article |
| id | doaj-art-da6e396ccbfb42ab807f81ee91af6d59 |
| institution | OA Journals |
| issn | 2307-4108 2307-4116 |
| language | English |
| publishDate | 2022-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Kuwait Journal of Science |
| spelling | doaj-art-da6e396ccbfb42ab807f81ee91af6d592025-08-20T02:30:32ZengElsevierKuwait Journal of Science2307-41082307-41162022-03-0149210.48129/kjs.10879An improved multi-object instance segmentation based on deep learningNawaf Alshdaifat0Mohd Azam Osman1Abdullah Zawawi Talib2Universiti Sains MalaysiaSchool of Computer Sciences Universiti Sains Malaysia,11800, Pulau Pinang, MalaysiaSchool of Computer Sciences Universiti Sains Malaysia,11800, Pulau Pinang, Malaysia Given the recent breakthroughs in recent years, Deep learning (DL) networks have attracted growing interest and attention by researchers and scholars alike due to its importance in detecting and instance segmentation of objects in an image. An entity's example instance segmentation is a critical problem that requires further analysis. However, given the difficulties in adopting object detection and the instance segmentation approach, this study aims to develop an approach to overcome these issues by proposing a new approach based on the recent DL approach in addition to developing an approach for object instance segmentation. The approach presented in this study consisted of three stages in order to improve the recognition approach. First, adopting a DL approach improves the object's detection in the enhanced ResNet (residual neural network) and connects it with the convolution layer for each ResNet block. Second, improving the localization of multiple objects dependent on the Region Proposal Network (RPN) approach, and third, utilizing a complex instance segmentation approach. This study's findings revealed that the suggested approach using a typical benchmark-image dataset, called the COCO dataset, the experiments are carried out and validated using generic evaluation parameters. The proposed approach's performance is verified and measured against the recent image segmentation approach using object instances. The findings also revealed that in terms of average precision over IoU (AP) threshold measurements using different thresholds, the proposed approach obtained improved results compared to other well-known segmentation approaches. https://journalskuwait.org/kjs/index.php/KJS/article/view/10879 |
| spellingShingle | Nawaf Alshdaifat Mohd Azam Osman Abdullah Zawawi Talib An improved multi-object instance segmentation based on deep learning Kuwait Journal of Science |
| title | An improved multi-object instance segmentation based on deep learning |
| title_full | An improved multi-object instance segmentation based on deep learning |
| title_fullStr | An improved multi-object instance segmentation based on deep learning |
| title_full_unstemmed | An improved multi-object instance segmentation based on deep learning |
| title_short | An improved multi-object instance segmentation based on deep learning |
| title_sort | improved multi object instance segmentation based on deep learning |
| url | https://journalskuwait.org/kjs/index.php/KJS/article/view/10879 |
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