This paper introduces a system which enables visually impaired users to detect objects and landmarks within the line of sight. The system works in two modes: landmark mode, which detects predefined landmarks, and object mode, which detects objects for everyday use. Users can get audio announcement for the name of the detected object or landmark as well as its estimated distances. Landmark detection helps visually impaired users explore an unfamiliar environment and build a mental map.
The proposed system utilizes a deep learning system for detection, which is deployed on the mobile phone and optimized to run in real-time. Unlike many other existing deep-learning systems that require an Internet connection or specific accessories. Our system works offline and only requires a smart phone with camera, which gives the advantage to avoid the cost for data services, reduce delay to access the cloud server, and increase the system reliability in all environments.
| Identifer | oai:union.ndltd.org:UMASS/oai:scholarworks.umass.edu:masters_theses_2-2035 |
| Date | 01 September 2020 |
| Creators | Zhang, Chenguang |
| Publisher | ScholarWorks@UMass Amherst |
| Source Sets | University of Massachusetts, Amherst |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Masters Theses |
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