Design and Implementation of an Intelligent Assistive Cane for Visually Impaired People Based on an Edge-Cloud Collaboration Scheme

Abstract

Visually impaired people face many inconveniences in daily life, and there are problems such as high prices and single functions in the market of assistance tools for visually impaired people. In this work, we designed and implemented a low-cost intelligent assistance cane, particularly for visually impaired individuals, based on computer vision, sensors, and an edge-cloud collaboration scheme. Obstacle detection, fall detection, and traffic light detection functions have been designed and integrated for the convenience of moving for visually impaired people. We have also designed an image captioning function and object detection function with high-speed processing capability based on an edge-cloud collaboration scheme to improve the user experience. Experiments show that the performance metrics have an aerial obstacle detection accuracy of 92.5%, fall detection accuracy of 90%, and average image retrieval period of 1.124 s. It proves the characteristics of low power consumption, strong real-time performance, adaptability to multiple scenarios, and convenience, which can ensure the safety of visually impaired people when moving and can help them better perceive and understand the surrounding environment.

Red Deer Optimization with Artificial Intelligence Enabled Image Captioning System for Visually Impaired People

“…CAM can be anticipated to be a latent distribution. In [16], the authors implemented and designed a cost-effective academic assistance cane, predominantly for blind individuals, related to an edge-cloud collaboration, scheme CV, and sensors. The authors have also devised an object detection and image captioning function with high-speed processing ability related to an edge-cloud collaboration technique to enhance the user experience.…”