Real Time Multi Object Detection for Blind Using Single Shot Multibox Detector
| Autor: | Atul Grover, Raksha Chugh, S. Sofana Reka, Adwitiya Arora |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
education.field_of_study
Artificial neural network business.industry Computer science Visual impairment Population Detector 020206 networking & telecommunications Speech synthesis 02 engineering and technology Image segmentation computer.software_genre Object detection Computer Science Applications 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Computer vision Artificial intelligence Electrical and Electronic Engineering medicine.symptom business education computer |
| Zdroj: | Wireless Personal Communications. 107:651-661 |
| ISSN: | 1572-834X 0929-6212 |
| DOI: | 10.1007/s11277-019-06294-1 |
| Popis: | According to world health statistics 285 million out of 7.6 billion population suffers visual impairment; hence 4 out of 100 people are blind. Absence of vision restricts the mobility of a person to pronounced extent and hence there is a need to build an explicit device to conquer guiding aid to the prospect. This paper proposes to build a prototype that performs real time object detection using image segmentation and deep neural network. Further the object, its position with respect to the person and accuracy of detection is prompted through speech stimulus to the blind person. The accuracy of detection is also prompted to the device holder. This work uses a combination of single-shot multibox detection framework with mobileNet architecture to build rapid real time multi object detection for a compact, portable and minimal response time device construction. |
| Databáze: | OpenAIRE |
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