# Deep-Learning-Based Cognitive Assistance Embedded Systems for People with Visual Impairment
## 2 Related Work
### Object Detection  
Deep learning-based systems for object identification have shown considerable versatility in a variety of applications, including health, construction, robotics, and designing smart cities. For assistive technologies, especially in medical applications, they enable independent navigation among persons with visual impairment by extending obstacle detection abilities as well as situational awareness augmentation [40; 62; 63]. Around construction, they improve structural evaluation as well as compliance with regulations, supported by UAV-based rebar detection [64] as well as monitoring systems for heavy equipment [65]. Recent studies have broadened their use in robotic perception as well as artificial intelligence integration, with working models proposed for LiDAR technology [66], road scene segmentation [67], and real-time instance segmentation [68]. Further, semantic segmentation techniques have been used to assess vegetation in urban areas as well as monitor ecological markers in urban planning in a smart city scenario [69] [W6], thus highlighting versatility as well as usability in assistive as well as industrial applications. Object identification has various applications, including in smart health-care systems. Many tools and systems for object detection have been proposed for helping people with visual impairment communicate and participate in society. [PERSON] et al. [70] suggested a computer-vision-based solution to help blind people navigate. Their system, which performs object detection and word recognition, was implemented on a portable computer and could detect objects that are critical for wayfinding, such as doors, elevators, and cabinets. To help people with visual impairment identify the detected objects more precisely, text recognition was applied to identify any textual information linked to the objects. [PERSON] and [PERSON] [71] created a smartphone-based wayfinding system for object detection to aid people with visual impairment. This system enables users to determine their location and then navigate to a given destination in an unknown interior setting. However, the system obtains wayfinding data from QR codes; thus, it can only be used in buildings in which wayfinding QR codes are installed, which limits its applicability. Several other systems have leveraged sensors and deep learning methods to enhance object detection effectiveness. [PERSON] et al. [7] demonstrated a ground-breaking prototype navigation system comprising a camera, an inertial measurement unit, and laser sensors as hardware. The system also contains two main software modules, namely a guidance module and a recognition module, for handling navigation and object recognition, respectively. [PERSON] et al. [62] created a framework for assisting people with visual impairment in identifying and avoiding obstacles. This system uses a millimeter-wave (MMW) radar and an RGB-Depth sensor for data collection. Objects are detected using the SSD and Mask R-CNN networks, and obstacle depth information is acquired using the MeanShift technique. Moreover, MMW radar data can be used for simultaneously estimating the positions and velocities of multiple detected targets, thereby substantially improving the system's performance. An embedded system presented by [PERSON] and [PERSON] [63] for users with visual impairment can track obstacles by using the object detection technique. The system is based on Raspberry Pi and is therefore wearable, lightweight, and small. [PERSON] et al. [40] presented an assistive artificial intelligence device to help people with visual impairment in better understanding their environment. This device uses a distance-measuring sensor to detect obstacles and employs YOLOv3 [36] to detect objects. Information regarding obstacles is transmitted to the user as audio. To achieve assisted navigation, [PERSON] et al. [72] proposed an automatic cognition system based on computer vision algorithms and a deep CNN running on a laptop.