Integrated deep learning approach for real-time object detection and color analysis.
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| Title: | Integrated deep learning approach for real-time object detection and color analysis. |
|---|---|
| Authors: | Byrappa, Srinivas Dibbur1 srinivas.db@nmit.ac.in, Gajendra, Kushal1 kushalkowsh@gmail.com, Puttaraju, Rohith Holenarasipura1 rohit.hp@nmit.ac.in, Malini, Tumakalahalli Nagaraj2 malini.tn@nmit.ac.in |
| Source: | International Journal of Electrical & Computer Engineering (2088-8708). Apr2026, Vol. 16 Issue 2, p863-872. 10p. |
| Subjects: | Object recognition (Computer vision), Analysis of colors, Convolutional neural networks, Computer vision, Artificial neural networks, Deep learning, Real-time computing |
| Abstract: | Object identification is one of the major application areas of deep learning that provides significantly better feature extraction and representation than more conventional methods of recognition. Driven by the growing significance of conjunction of objects detection and color interpretation in contemporary computer vision systems, the current work proposes an integrated, real-time deep learning system that completes the task of object localization and color analysis. It is suggested that the proposed system employs a faster region-based convolutional neural network (Faster R-CNN) with backbone of ResNet-50 and supplemented with a feature pyramid network to perform multi-scale feature aggregation. The model was trained and tested using the Pascal VOC 2012 dataset and it showed good results with the average precision of 0.8114, F1 of 0.6232 and IoU of 0.7096. The large set of experiments on different learning rates and training epochs allowed optimizing the detector to work well in a variety of conditions. To enhance even more, visualization histogram of oriented gradients (HOG) and gradient-weighted class activation mapping (Grad-CAM) was used to gain a more profound understanding of the significance of features and the logic behind a model. This study complements image perception with color by combining object recognition and color in a single architecture, which can result in fruitful applications in areas of autonomous vehicles, industrial automation, and medical imaging. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 192718351 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Integrated deep learning approach for real-time object detection and color analysis. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Byrappa%2C+Srinivas+Dibbur%22">Byrappa, Srinivas Dibbur</searchLink><relatesTo>1</relatesTo><i> srinivas.db@nmit.ac.in</i><br /><searchLink fieldCode="AR" term="%22Gajendra%2C+Kushal%22">Gajendra, Kushal</searchLink><relatesTo>1</relatesTo><i> kushalkowsh@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Puttaraju%2C+Rohith+Holenarasipura%22">Puttaraju, Rohith Holenarasipura</searchLink><relatesTo>1</relatesTo><i> rohit.hp@nmit.ac.in</i><br /><searchLink fieldCode="AR" term="%22Malini%2C+Tumakalahalli+Nagaraj%22">Malini, Tumakalahalli Nagaraj</searchLink><relatesTo>2</relatesTo><i> malini.tn@nmit.ac.in</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Electrical+%26+Computer+Engineering+%282088-8708%29%22">International Journal of Electrical & Computer Engineering (2088-8708)</searchLink>. Apr2026, Vol. 16 Issue 2, p863-872. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Object+recognition+%28Computer+vision%29%22">Object recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Analysis+of+colors%22">Analysis of colors</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+vision%22">Computer vision</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Real-time+computing%22">Real-time computing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Object identification is one of the major application areas of deep learning that provides significantly better feature extraction and representation than more conventional methods of recognition. Driven by the growing significance of conjunction of objects detection and color interpretation in contemporary computer vision systems, the current work proposes an integrated, real-time deep learning system that completes the task of object localization and color analysis. It is suggested that the proposed system employs a faster region-based convolutional neural network (Faster R-CNN) with backbone of ResNet-50 and supplemented with a feature pyramid network to perform multi-scale feature aggregation. The model was trained and tested using the Pascal VOC 2012 dataset and it showed good results with the average precision of 0.8114, F1 of 0.6232 and IoU of 0.7096. The large set of experiments on different learning rates and training epochs allowed optimizing the detector to work well in a variety of conditions. To enhance even more, visualization histogram of oriented gradients (HOG) and gradient-weighted class activation mapping (Grad-CAM) was used to gain a more profound understanding of the significance of features and the logic behind a model. This study complements image perception with color by combining object recognition and color in a single architecture, which can result in fruitful applications in areas of autonomous vehicles, industrial automation, and medical imaging. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.11591/ijece.v16i2.pp863-872 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 863 Subjects: – SubjectFull: Object recognition (Computer vision) Type: general – SubjectFull: Analysis of colors Type: general – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Computer vision Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Real-time computing Type: general Titles: – TitleFull: Integrated deep learning approach for real-time object detection and color analysis. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Byrappa, Srinivas Dibbur – PersonEntity: Name: NameFull: Gajendra, Kushal – PersonEntity: Name: NameFull: Puttaraju, Rohith Holenarasipura – PersonEntity: Name: NameFull: Malini, Tumakalahalli Nagaraj IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20888708 Numbering: – Type: volume Value: 16 – Type: issue Value: 2 Titles: – TitleFull: International Journal of Electrical & Computer Engineering (2088-8708) Type: main |
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