Academic Journal
Real-Time Object Detection For The Visually Impaired Using Yolov8 And NLP On Iot Devices.
| Τίτλος: | Real-Time Object Detection For The Visually Impaired Using Yolov8 And NLP On Iot Devices. |
|---|---|
| Συγγραφείς: | Venkatkrishnan, G. R., Jeya, R., Ramyalakshmi, G., Sindhu, S., Sreenithi, K. |
| Πηγή: | International Journal of Environmental Sciences (2229-7359); 2025 Special Issue, Vol. 11, p907-914, 8p |
| Θεματικοί όροι: | People with visual disabilities, Assistive technology, Automatic speech recognition, Internet of things, Object recognition (Computer vision), Real-time computing |
| Περίληψη: | Object detection is an important development in real-time that integrates and Artificial Intelligence (AI), embedded systems, and Internet of Things (IoT). These innovations aim to address the challenges faced by visually impaired individuals in locating everyday objects independently. Current systems often suffer from limitations like manual tagging, lack of realtime feedback, and poor adaptability in dynamic environments. This paper introduces an IoTbased Voice-Driven Smart Finder that leverages Natural Language Processing (NLP), YOLOv8 object detection, and cloud-based speech recognition for efficient and autonomous object location. The primary objective is to create a voice-interactive, low-cost solution that enhances the independence of visually impaired users by allowing them to locate objects using simple verbal queries. The system’s novelty lies in its integration of real-time object detection, speech-based control, and optimized edge-device deployment without the need for predefined object tagging. The proposed model achieved a detection accuracy of 92% on household objects and a fast response time of approximately 1.8 seconds, highlighting its practical effectiveness. By utilizing affordable hardware like Raspberry Pi 4 and integrating cloud APIs for speech processing, this work contributes a scalable and inclusive solution to the assistive technology landscape. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Environmental Sciences (2229-7359) is the property of Academic Science Publications & Distributions (ASPD) 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.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: edb DbLabel: Complementary Index An: 189095231 RelevancyScore: 1007 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1007.33666992188 |
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| Items | – Name: Title Label: Title Group: Ti Data: Real-Time Object Detection For The Visually Impaired Using Yolov8 And NLP On Iot Devices. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Venkatkrishnan%2C+G%2E+R%2E%22">Venkatkrishnan, G. R.</searchLink><br /><searchLink fieldCode="AR" term="%22Jeya%2C+R%2E%22">Jeya, R.</searchLink><br /><searchLink fieldCode="AR" term="%22Ramyalakshmi%2C+G%2E%22">Ramyalakshmi, G.</searchLink><br /><searchLink fieldCode="AR" term="%22Sindhu%2C+S%2E%22">Sindhu, S.</searchLink><br /><searchLink fieldCode="AR" term="%22Sreenithi%2C+K%2E%22">Sreenithi, K.</searchLink> – Name: TitleSource Label: Source Group: Src Data: International Journal of Environmental Sciences (2229-7359); 2025 Special Issue, Vol. 11, p907-914, 8p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22People+with+visual+disabilities%22">People with visual disabilities</searchLink><br /><searchLink fieldCode="DE" term="%22Assistive+technology%22">Assistive technology</searchLink><br /><searchLink fieldCode="DE" term="%22Automatic+speech+recognition%22">Automatic speech recognition</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+of+things%22">Internet of things</searchLink><br /><searchLink fieldCode="DE" term="%22Object+recognition+%28Computer+vision%29%22">Object recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Real-time+computing%22">Real-time computing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Object detection is an important development in real-time that integrates and Artificial Intelligence (AI), embedded systems, and Internet of Things (IoT). These innovations aim to address the challenges faced by visually impaired individuals in locating everyday objects independently. Current systems often suffer from limitations like manual tagging, lack of realtime feedback, and poor adaptability in dynamic environments. This paper introduces an IoTbased Voice-Driven Smart Finder that leverages Natural Language Processing (NLP), YOLOv8 object detection, and cloud-based speech recognition for efficient and autonomous object location. The primary objective is to create a voice-interactive, low-cost solution that enhances the independence of visually impaired users by allowing them to locate objects using simple verbal queries. The system’s novelty lies in its integration of real-time object detection, speech-based control, and optimized edge-device deployment without the need for predefined object tagging. The proposed model achieved a detection accuracy of 92% on household objects and a fast response time of approximately 1.8 seconds, highlighting its practical effectiveness. By utilizing affordable hardware like Raspberry Pi 4 and integrating cloud APIs for speech processing, this work contributes a scalable and inclusive solution to the assistive technology landscape. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of International Journal of Environmental Sciences (2229-7359) is the property of Academic Science Publications & Distributions (ASPD) 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: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 8 StartPage: 907 Subjects: – SubjectFull: People with visual disabilities Type: general – SubjectFull: Assistive technology Type: general – SubjectFull: Automatic speech recognition Type: general – SubjectFull: Internet of things Type: general – SubjectFull: Object recognition (Computer vision) Type: general – SubjectFull: Real-time computing Type: general Titles: – TitleFull: Real-Time Object Detection For The Visually Impaired Using Yolov8 And NLP On Iot Devices. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Venkatkrishnan, G. R. – PersonEntity: Name: NameFull: Jeya, R. – PersonEntity: Name: NameFull: Ramyalakshmi, G. – PersonEntity: Name: NameFull: Sindhu, S. – PersonEntity: Name: NameFull: Sreenithi, K. IsPartOfRelationships: – BibEntity: Dates: – D: 02 M: 08 Text: 2025 Special Issue Type: published Y: 2025 Identifiers: – Type: issn-print Value: 22297359 Numbering: – Type: volume Value: 11 Titles: – TitleFull: International Journal of Environmental Sciences (2229-7359) Type: main |
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