Enhancing Kitchen Independence: Deep Learning-Based Object Detection for Visually Impaired Assistance
Bo Dang, Danqing Ma, Shaojie Li, Xinqi Dong, Hengyi Zang, Rui Ding
Abstract
Visually impaired individuals face substantial challenges in kitchens, where identifying objects accurately is crucial yet difficult due to the complexity and variability of the environment. Traditional object detection1 methods fall short in these settings, struggling with the assortment of items. This research highlights the need for advanced, kitchen-specific solutions that leverage deep learning to improve detection accuracy and offer real-time, interactive guidance through speech technologies. By focusing on the unique demands of kitchen environments, the proposed system aims to significantly enhance the autonomy and safety of visually impaired users, presenting a notable advancement in assistive technology. The effectiveness of this approach is assessed by its ability to accurately identify kitchen items for visually impaired individuals.