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@article{209267,
        author = {Nidhi Y Pandya},
        title = {GeoVision Cap- A Technology gifted eye},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {5},
        pages = {1114-1119},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=209267},
        abstract = {Visually impaired people rely on white canes, guide dogs and, increasingly, wearable electronics for safe mobility, yet most low-cost devices only warn of nearby obstacles and say little about where the user is or what surrounds them. This paper reviews five recent studies: three on visual geo-localization (global image geolocation, unmanned aerial vehicle (UAV) cross-view matching, and Mamba-based deep hashing) and two on cap-based assistive devices for blind users. The studies are compared by task, platform, method, dataset, reported results and suitability for wearable use. The review shows that geo-localization methods achieve strong benchmark accuracy but are developed for aircraft, vehicles or archived imagery on graphics processing unit (GPU) hardware, whereas assistive caps are inexpensive but limited to proximity warnings or slow object labelling, with little quantitative evaluation. Seven research gaps are identified. The design of GeoVision Cap, a Raspberry Pi 5 based edge artificial intelligence (Edge AI) wearable that combines vision, location and voice interaction, is then presented as a planned response to these gaps.},
        keywords = {Assistive technology, Edge AI, Geo-localization, Raspberry Pi, Sensor fusion, Vision-language models, visually impaired, Wearable devices.},
        month = {October},
        }