Recognition of surrounding environment from electric wheelchair videos based on modified YOLOv2

Currently, the aging population is growing in Japan, and the needs for the utilization of welfare equipment are consequently increasing. The electric wheelchair, which is a convenient transportation tool, has rapidly become popular. However, many accidents have occurred when using electric wheelchai...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Future generation computer systems Jg. 92; S. 157 - 161
Hauptverfasser: Sakai, Yuki, Lu, Huimin, Tan, Joo-Kooi, Kim, Hyoungseop
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.03.2019
Schlagworte:
Autonomous wheelchair
Convolutional neural network
Object detection
YOLOv2
YOLOv2
Autonomous wheelchair
Convolutional neural network
Object detection
ISSN:0167-739X, 1872-7115
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Currently, the aging population is growing in Japan, and the needs for the utilization of welfare equipment are consequently increasing. The electric wheelchair, which is a convenient transportation tool, has rapidly become popular. However, many accidents have occurred when using electric wheelchairs, and the dangers of driving have been noted. Therefore, there is a need to improve accident factors, reduce accidents and improve the convenience of electric wheelchairs by using automation. Environmental recognition is the key technology for developing autonomous electric wheelchairs. Environmental recognition includes self-position estimation, the recognition of sidewalks, crosswalks and traffic lights, and moving object predictions. To solve these problems, this paper develops a system for detecting sidewalks, crosswalks and traffic lights. We develop the object recognition methods using a modified YOLOv2, which is an object detection algorithm that applies convolutional neural networks (CNNs). We detect the object through YOLOv2 and perform processing steps, such as unnecessary bounding box deletion and interpolation. The experimental results demonstrate that the average AUC of the detection rate is 0.587. •We develop a system for detecting sidewalks, crosswalks and traffic lights.•We develop the object recognition methods using a modified YOLOv2.•We detect the object use bounding box deletion and interpolation.
ISSN:0167-739X
1872-7115
DOI:10.1016/j.future.2018.09.068