# Implementation of an Obstacle Recognition System for the Blind
## 1 Introduction  
Many people with disabilities have some difficulty walking unaided and are exposed to various traffic accident risks. They are often classified as mobility impaired. Disability protection areas are necessary to protect the disabled from danger while walking; however, according to data in Seoul, only seven of the 631 disabled facilities have been designed as disability protection areas [1]. The lack of safeguards for people with disabilities can deprive them of many positive opportunities, including walking independently. As the population increases, the number of blind and visually impaired people also increases. Researchers have predicted that the number of people with a visual impairment or blindness will increase from 38.5 million in 2020 to more than 115 million by 2050 [2]. Among the various types of disabilities, we took into consideration the walking safety of the blind and visually impaired. We focused particularly on problems that could arise while walking outside.  
The blind can choose from various methods that can help guide them while walking outdoors. One way is the utilization of guide dogs. Guide dogs are trained by professional trainers for an average of two years until they are skilled enough to safely guide the blind. Some blind people prefer to buy a guide dog owing to its familiar character. However, people with an aversion to animals may not want to utilize a guide dog. Moreover, if the blind or their family members have a health problem, such as an allergy to dogs, they cannot choose this method of assistance. Additionally, it costs money to buy and support a dog, and not everyone can afford such an expense. Furthermore, there are not enoughguide dogs available to assist the blind; therefore, there needs to be alternative ways to support the visually impaired and blind.  
The blind must also consider the various obstacles they will encounter while walking outdoors. Theoretically, they can use acoustic signals when they want to cross streets independently at crosswalks, however, the emplacement rate of acoustic signals is only 57% [3]. People with disabilities can operate the acoustic signals directly by pressing a button or using a device, such as a remote control. However, the acoustic signal button is installed in the light pole near a crosswalk, so it cannot be operated if it is inaccessible to the blind or if it is broken.  
Another obstacle that interferes with safety while walking is the use of bollards, which are short posts. Bollards are installed to keep pedestrians safe by preventing cars and other dangerous objects from entering pedestrian pathways. However, they are installed in the middle of sidewalks, which can pose a big danger to the blind who cannot see them. There have also been instances where non-visually impaired people have collided with bollards and fallen because the bollards were not properly marked.  
In this study, we implemented a system that recognizes crosswalk lights and bollards. We used images of crosswalk lights and bollards to detect actual crosswalk lights and bollards to safely guide the blind. An engine for assessing whether images were considered to be obstacles was implemented by retraining the existing object detection models [4; 5] of deep learning. After acknowledging the presence of a crosswalk light, the module that detects a crosswalk light determines whether it is safe for the blind to cross or not and directs them by voice [6]. The bollard recognition module informs the blind where the obstacle is by using the faster region-convolutional neural network (R-CNN) model to recognize it. Our system enables real time image recognition and guidance with Raspberry Pi or an Android application. Here, we introduce our system and how to implement it. We also provide its performance evaluation. Using this system, we could provide a safe outdoor walking environment for the blind.