Obálka

Computer Vision-Based Obstacle Detection Mobile System for Visually Impaired Individuals

Uloženo v:
Vydáno v:Multimodal Technologies and Interaction vol. 9, no. 5 (2025), p. 48
Hlavní autor: Bastidas-Guacho, Gisel Katerine
Další autoři: Paguay Alvarado Mario Alejandro, Moreno-Vallejo, Patricio Xavier, Moreno-Costales, Patricio Rene, Ocaña Yanza Nayely Samanta, Troya Cuestas Jhon Carlos
Vydáno:
MDPI AG
Témata:
Visual impairment
Cameras
Students
Usability
Deep learning
Datasets
People with disabilities
Performance evaluation
Applications programs
Computer vision
Cell phones
Mobile communications networks
Sensors
Adaptive technology
Task complexity
Mobile computing
Blindness
Images
Object recognition
Real time
Feedback
Obstacle avoidance
On-line přístup:Citation/Abstract
Full Text + Graphics
Full Text - PDF
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Abstrakt:Traditional tools, such as canes, are no longer enough to subsist the mobility and orientation of visually impaired people in complex environments. Therefore, technological solutions based on computer vision tasks are presented as promising alternatives to help detect obstacles. Object detection models are easy to couple to mobile systems, do not require a large consumption of resources on mobile phones, and act in real-time to alert users of the presence of obstacles. However, existing object detectors were mostly trained with images from platforms such as Kaggle, and the number of existing objects is still limited. For this reason, this study proposes to implement a mobile system that integrates an object detection model for the identification of obstacles intended for visually impaired people. Additionally, the mobile application integrates multimodal feedback through auditory and haptic interaction, ensuring that users receive real-time obstacle alerts via voice guidance and vibrations, further enhancing accessibility and responsiveness in different navigation contexts. The chosen scenario to develop the obstacle detection application is the Specialized Educational Unit Dr. Luis Benavides for impaired people, which is the source of images for building the dataset for the model and evaluating it with impaired individuals. To determine the best model, the performance of YOLO is evaluated by means of a precision adjustment through the variation of epochs, using a proprietary data set of 7600 diverse images. The YOLO-300 model turned out to be the best, with a mAP of 0.42.
ISSN:2414-4088
DOI:10.3390/mti9050048
Zdroj:Advanced Technologies & Aerospace Database