Computer Vision-Based Assistance System for the Visually Impaired Using Mobile Edge Artificial Intelligence
Despite significant recent developments, visual assistance systems are still severely constrained by sensor capabilities, form factor, battery power consumption, computational resources and the use of traditional computer vision algorithms. Current visual assistance systems cannot adequately perform complex computer vision tasks that entail deep learning. We present the design and implementation of a novel visual assistance system that employs deep learning and point cloud processing to perform advanced perception tasks on a cost-effective, low-power mobile computing platform. The proposed system design circumvents the need for expensive, power-intensive Graphical Processing Unit (GPU)-based hardware required by most deep learning algorithms for real-time inference by employing instead edge Artificial Intelligence (AI) accelerators such as the Neural Compute Stick-2 (NCS2), model optimization techniques such as OpenVINO, and TensorflowLite, and smart depth sensors such as OpenCV AI Kit-Depth (OAK-D). Critical system design challenges such as training data collection, real-time capability, computational efficiency, power consumption, portability and reliability are addressed. The proposed system includes more advanced functionality than existing systems such as assessment of traffic conditions and detection and localization of hanging obstacles, crosswalks, moving obstacles and sudden elevation changes. The proposed system design incorporates an AI-based voice interface that allows for user-friendly interaction and control and is shown to realize a simple, cost-effective, power-efficient, portable and unobtrusive visual assistance device.