AI-Powered Object Detection
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| Format: | Recurso digital |
| Sprache: | Englisch |
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2026
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| _version_ | 1866902036874788864 |
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| author | Rajaram Roopa Sri Mungi Sai Akshaya, Dr V. Subba Ramaiah Ms. S. Renuka Dr. K Mahesh Kumar |
| author_facet | Rajaram Roopa Sri Mungi Sai Akshaya, Dr V. Subba Ramaiah Ms. S. Renuka Dr. K Mahesh Kumar |
| contents | <h2><span>This paper presents an AI-powered Object Detection system designed to provide real-time environmental awareness using computer vision and machine learning techniques. The system captures live video input through a camera and processes each frame to perform object detection, text recognition using OCR, currency identification, and traffic light detection. It includes a direction and urgency analysis module to determine the position and importance of detected objects, enabling effective decision-making. A priority-based alert queue system is implemented to manage and deliver voice alerts using a text-to-speech engine without overlap. The system supports multiple modes such as walk mode, OCR mode, and currency mode, allowing flexible and context-aware<span> </span>operation.<span> </span>Experimental<span> </span>results<span> </span>demonstrate<span> </span>that<span> </span>the system performs efficiently in real-time scenarios with minimal delay, making it a practical and reliable assistive solution, particularly for visually impaired individuals.</span></h2> <p class="MsoNormal"><strong><em><span> </span></em></strong></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20227828 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | AI-Powered Object Detection Rajaram Roopa Sri Mungi Sai Akshaya, Dr V. Subba Ramaiah Ms. S. Renuka Dr. K Mahesh Kumar Artificial Intelligence, Computer Vision, Object Detection, Optical Character Recognition (OCR), Currency Detection, Traffic Light Recognition, Text-to-Speech (TTS), Real-Time Processing, Assistive Technology, Alert Queue System, OpenCV, YOLO, Smart Vision Assistant <h2><span>This paper presents an AI-powered Object Detection system designed to provide real-time environmental awareness using computer vision and machine learning techniques. The system captures live video input through a camera and processes each frame to perform object detection, text recognition using OCR, currency identification, and traffic light detection. It includes a direction and urgency analysis module to determine the position and importance of detected objects, enabling effective decision-making. A priority-based alert queue system is implemented to manage and deliver voice alerts using a text-to-speech engine without overlap. The system supports multiple modes such as walk mode, OCR mode, and currency mode, allowing flexible and context-aware<span> </span>operation.<span> </span>Experimental<span> </span>results<span> </span>demonstrate<span> </span>that<span> </span>the system performs efficiently in real-time scenarios with minimal delay, making it a practical and reliable assistive solution, particularly for visually impaired individuals.</span></h2> <p class="MsoNormal"><strong><em><span> </span></em></strong></p> |
| title | AI-Powered Object Detection |
| topic | Artificial Intelligence, Computer Vision, Object Detection, Optical Character Recognition (OCR), Currency Detection, Traffic Light Recognition, Text-to-Speech (TTS), Real-Time Processing, Assistive Technology, Alert Queue System, OpenCV, YOLO, Smart Vision Assistant |
| url | https://doi.org/10.5281/zenodo.20227828 |