A Novel Marker Detection System for People with Visual Impairment Using the Improved Tiny-YOLOv3 Model. (June 2021)

• Record Type: Journal Article
• Authors: Elgendy, Mostafa; Sik-Lanyi, Cecilia; Kelemen, Arpad
• Is Part Of: Computer methods and programs in biomedicine. Volume 205(2021)
• Publication Date: 2021-06
• Subjects: Assistive technology -- Visually impaired -- Indoor navigation -- Markers -- Deep learning -- Tiny-YOLOv3; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.106112
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A system is proposed using Tiny-YOLOv3 model to help people with visual impairment for easy indoor navigation. Modified versions of the original Tiny-YOLOv3 model are proposed to improve detection accuracy. The proposed models are more efficient and more robust than the original Tiny-YOLOv3 model. Abstract: Background and Objective: Daily activities such as shopping and navigating indoors are challenging problems for people with visual impairment. Researchers tried to find different solutions to help people with visual impairment navigate indoors and outdoors. Methods: We applied deep learning to help visually impaired people navigate indoors using markers. We propose a system to help them detect markers and navigate indoors using an improved Tiny-YOLOv3 model. A dataset was created by collecting marker images from recorded videos and augmenting them using image processing techniques such as rotation transformation, brightness, and blur processing. After training and validating this model, the performance was tested on a testing dataset and on real videos. Results: The contributions of this paper are: (1) We developed a navigation system to help people with visual impairment navigate indoors using markers; (2) We implemented and tested a deep learning model to detect Aruco markers in different challenging situations using Tiny-YOLOv3; (3) We implemented and compared several modified versions of the original model to improve detection accuracy. The modified Tiny-YOLOv3 model achieved an accuracy of 99.31% in challenging conditions and the original model achieved an accuracy of 96.11 %. Conclusion: The training and testing results show that the improved Tiny-YOLOv3 models are superior to the original model.