# Distance Assessment by Object Detection--For Visually Impaired Assistive Mechatronic System
## 2 State of the Art  
Recognizing subjects from images obtained with cameras is quite a new domain for researchers. Computer visualization and computer image processing have given rise to the information technology subdomain. By studying the homogeneity of colors, the distribution of color pattern components, the spatial relationships between the observed areas, color grouping, and the boundaries between color areas, we found certain relationships between the coloristics that exist in images and the possible existing objects in the frame [4]. Over time, techniques have been developed to enable the recognition of persons and facial recognition [5], for tracking moving objects, counting vehicles on surveillance cameras and obtaining information on their registration number [6], for recognizing activity [7], etc. There are two major trends in the development of object recognition methods, depending on the technology used: systems based on the function for visualization of the environment (cameras/video), and systems without visualization of the environment (IR sensors, laser, ultrasonic, Bluetooth, or GPS) [8]. Such systems are included in practical applications in many fields, but two are particularly distinct: applications in the field of freight and passenger transport for self-driving cars, and in the medical field for the assistance of people with visual impairments. The field of self-driving transport systems is a daily reality. A method of assessing the distance from obstacles to autonomous systems using video cameras is presented in [9]. Methods for recognizing objects from information obtained with the help of video cameras are few: from methods of the intuitive processing of still or moving images with the help of technologies based on neural networks [10], to methods that use parallel systems of multiple recognition for less error. The key task is to enable the vehicle to understand the environment correctly and make real-time decisions [11]. A multitude of sensors can be used simultaneously: video cameras, LIDAR lasers, radar, inertial measurement units (IMU), global satellite navigation (GPS), etc. Objects existing in the working environment of the mechatronic and robotic systems must be detected and evaluated as a shape and a name, sometimes only recognized by an area or portion, classified according to the templates in the existing databases [12] and possibly corrected parametrically. Object recognition is used by artificial intelligence systems for machine learning through appropriate algorithms and software based on real-world data in order to improve their own performance through experience and for advanced deep learning [13; 14; 15], which is performed using advanced methods that often include artificial neural networks with multiple layers to facilitate the robotic system automatic identification in the future of all 3D material elements encountered. From a technical point of view, the visual perception equipment used in the research includes simple cameras, high-performance HQ cameras, infrared cameras, and IMU (inertial measurement units) or LIDAR sensors [16; 17] to enhance contour and shape recognition. There are many studies in the literature that incorporate the method with stereo cameras [18; 19] or with multiple cameras [20], which \"attack\" the object from several angles in order to improve its descriptive technical parameters. Among the parameters analyzed in the research are depth, overall size, image distortion [21], and the accuracy of estimating the position of the object. In order for the intelligent system to \"see\", experimental applications based on digital images are focused on the identification of moving deformable objects, door openings, the detection of vegetables and fruits [22], the avoidance of obstacles, and the sorting of construction gravel aggregates. Several interconnected modules, including the visual perception subsystem and the learning subsystem, are required to control robotic systems.