###### Abstract  
We present in this paper the state of the art and an analysis of recent research work and achievements performed in the domain of AI-based and vision-based systems for helping blind and visually impaired people (BVIP). We start by highlighting the recent and tremendous importance that AI has acquired following the use of convolutional neural networks (CNN) and their ability to solve image classification tasks efficiently. After that, we also note that VIP have high expectations about AI-based systems as a possible way to ease the perception of their environment and to improve their everyday life. Then, we set the scope of our survey: we concentrate our investigations on the use of CNN or related methods in a vision-based system for helping BVIP. We analyze the existing surveys, and we study the current work (a selection of 30 case studies) using several dimensions such as acquired data, learned models, and human-computer interfaces. We compare the different approaches, and conclude by analyzing future trends in this domain. blind and visually impaired people; assistive technologies; artificial intelligence 20221230820222022123082022212308221230822123082212308221230822123082212308221230822122308221223082212230822122308212230821223082122308212230821  
distances) with other sensory stimuli such as acoustic or haptic signals, and conceptual ones that develop orientation strategies (spatial modeling or surface mapping) to represent the environment and prepare for unpredictable situations during navigation. Assistive systems need three modules to help blind and visually impaired people (BVIP). The first is the navigation module, or wayfinding, defined by [PERSON] and [PERSON] [3] as \"the set of efficient movements required to reach the desired destination, which benefits from the knowledge of the user's initial location and constant update of the user's orientation\". This module should ideally provide path and surface description, a selection of the optimal path taking several criteria into account (user preference, avoidance of hazardous zones, etc. ), accident-free navigation, and completion within a reasonable amount of time. Ideally, it must also work indoors and outdoors, in various light conditions (night/day, sun/rain, etc. ), regardless the location has already been visited or not, and perform real-time analysis without sacrificing robustness and accuracy. The second module performs object detection with two purposes. First, it allows the avoidance of obstacles that might cause harm to the user, and warn the user in a helpful way. It must be able to detect static and dynamic obstacles and their locations (preferably ahead) and nature, and estimate their distance to provide timely feedback. Object detection should also provide scene description whenever the user asks, to give the BVIP a good understanding of their environment and build cognitive maps of a place. The last module is the human-machine interface that encompasses the tools that will be manipulated by the user. This is comprised of several devices meant to acquire data, process it, and finally return the information to the user. Many combinations are possible, and they must be chosen according to several criteria, such as algorithmic methods, user preference or wearability of devices. Several surveys have been conducted in recent years on assistive technologies for BVIP, and the range of technologies available that could be used to develop them. Some of those articles were read when preparing this paper, to understand the different ways this subject has been addressed in the past. In their articles, [PERSON] and [PERSON] [4] and [PERSON] et al. [5] provided detailed descriptions of their research methodologies, highlighting the connections between the different fields pertaining to assistive technologies for BVIP. They spotted the most frequent keywords found in the papers they analyzed and their common sources, and stressed the trends of research. Some papers concentrated on the technological aspects of navigation and object detection.