A Study on Intelligent Guide Stick Using YOLOv3 Algorithm – Improving Spatial Awareness with Self-made Data Set

Abstract

The increasing negligence of “blind lanes” on streets in metropolises such as Shanghai is an inconvenience to the blind population, therefore, alternatives should be explored. When walking on these lanes, there should not be any obstacles for the users, yet bikes can be seen parked there in addition to littered objects. This makes it potentially more dangerous to walk on them in comparison to walking on non-tactile paved lanes. An alternative to these lanes has been discovered which is a camera that is attached to a blind staff. This camera provides auditory feedbacks in regard to the user’s surroundings. Using YOLOv3 (You Only Look Once, Version 3), the software is trained using 140 images to identify three different classes which are blind lanes, waist-high obstacles, and dog feces, as well as the right direction of these objects. If the camera captures any of these three categories, it will provide and voice feedback, hence, warning the user. With this system, the blind can essentially have functional vision that would better guarantee their safety when walking on streets.

YOLOv3-MSSA based hot spot defect detection for photovoltaic power stations

“…Their findings can effectively perform bridge deck crack detection with high detection efficiency. Yan [13] proposed an obstacle recognition method using the YOLO algorithm to help blind individuals navigate obstacle-filled paths. The method employd 140 obstacle images during training and utilized speech to provide recognition information feedback, resulting in accurate obstacle recognition and precise speech guidance.…”