Enhanced Yolov8 with OpenCV for Blind-Friendly Object Detection and Distance Estimation
Dublin Core
Title
Enhanced Yolov8 with OpenCV for Blind-Friendly Object Detection and Distance Estimation
Subject
computer vision; blindpeople; YOLOv8; OpenCV
Description
The development of computer technology and computer vision has had a significant positive impact on the daily lives of blind people, especially in efforts to improve their navigation abilities. This research has the main aim of introducing a superiorobject detection method, especially in supporting the sustainability and effectiveness of navigation for the blind. The main focus of the research is the use of YOLOv8, the latest version of YOLO, as an object detection method, and distance measurement technology from OpenCV. The main challenge to be addressed involves improving the accuracy and performance of object detection, which is an important key to ensuring safe and effective navigation for blind people. In this context, blind people often face obstacles in their mobility, especially when walking around environments that may be full of obstacles or obstacles. Therefore, better object detection methods become essential to ensure the identification of nearby objects, which may involveobstacles or potential threats, thereby preventing possible accidents or difficulties in daily commuting. Involving YOLOv8 as an object detection method provides the advantage of a high level of accuracy, although with a slight increase in detection duration and GPU power consumption compared to previous versions. The research results show that YOLOv8 provides a low error rate, with an average error percentage of 3.15%, indicating very optimal results. Using a combined performance evaluation approach of YOLOv8 and OpenCV distance measurement metrics, this research not only seeks to improve accuracy but also efficiency in detection time and power consumption. This research makes an important contribution in presenting technological solutions that can help improve mobility and safety for blind people, bringing a real positive impact in facilitating their daily lives
Creator
Erwin Syahrudin1, Ema Utami2, Anggit Dwi Hartanto
Source
https://jurnal.iaii.or.id/index.php/RESTI/article/view/5529/917
Publisher
Magister of Informatics Engineering, Universitas AMIKOM Yogyakarta, Yogyakarta, Indonesia
Date
29-03-2024
Contributor
FAJAR BAGUS W
Format
PDF
Language
ENGLISH
Type
TEXT
Files
Collection
Citation
Erwin Syahrudin1, Ema Utami2, Anggit Dwi Hartanto, “Enhanced Yolov8 with OpenCV for Blind-Friendly Object Detection and Distance Estimation,” Repository Horizon University Indonesia, accessed January 26, 2026, https://repository.horizon.ac.id/items/show/10400.