# Implementation of an Obstacle Recognition System for the Blind
## 6 Conclusions  
In this study, we proposed recognition software for crossing lights and bollards, along with a hardware system. The proposed system did not need to be built, nor was a client required to wear any expensive equipment. Additionally, it was possible to recognize the two aforementioned obstacles by using a video input device, namely, a single camera, rather than several types of sensor, as necessitated by previous systems.  
We initially used pre-trained models that recognized the two obstacles. We retrained the SSD MobileNet and Faster R-CNN models using the Google Object Detection API. The client had a Raspberry Pi and an Android app, and the camera module of the Raspberry Pi streamed images in real time. Then, the obstacle recognition model in the server recognized obstacles in the images. If the obstacle was a crosswalk light, it used the crosswalk discrimination module to determine if it was a red, green, or flashing light. In the case of a bollard, it identified the position of the bollard in the image and returned the location information. The results were passed back to the Raspberry Pi and Android app via the network module and guided the client by voice guidance playback to safely overcome the obstacle.  
In this study, we revealed moments of obstacle recognition displayed on obstacle detection screens from our system. We also compared the performance of the SSD MobileNet to that of the Faster R-CNN model and found that the Faster R-CNN recognition results were significantly better. The performance of the crosswalk safety guidance algorithm was approximately 80% during the daytime. This result seemed somewhat low, but the result of risking the safety of the client among the mismatched results was 0%. The currently low results could be improved and resolved in the future by improving the current algorithms.  
In the future, we need to extend this system in many ways. First, the system was implemented on the premise that one obstacle was recognized in one image. We are devising how to calculate and guide directionality when several obstacles are recognized in an image. Second, the algorithms of the system need to be improved. The system must guide the client even more safely when using the  
\begin{table}
\begin{tabular}{c c} \hline  & **Bollard** \\ \hline Day & 100\% \\ Night & 100\% \\ \hline \end{tabular}
\end{table}
Table 4: Accuracy measurement for bollard location finding.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Flashing** & **Green** & **Red** \\ \hline Day & 82\% & 84\% & 80\% \\ Night & 42\% & 68\% & 52\% \\ \hline \end{tabular}
\end{table}
Table 3: Accuracy measurement for crosswalk safety guidance.  
crosswalk safety guidance algorithm. With these types of improvements to our current system on the horizon, the potential for the blind walking outdoors independently is great.  
Software, S.O. and H.P.; Supervision, J.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Research Foundation of Korea (NRF) funded by the MSIT (NRF-2018R1A4A1025559).  
The authors declare no conflict of interest.