Identification of Markers in Challenging Conditions for People with Visual Impairment Using Convolutional Neural Network

Obstacle Segmentation with Encoder-Decoder Architectures in Low Structured Environments for the Navigation of Visually Impaired People

Orientation and mobility of visually impaired people usually requires intensive training with mobility aids (e.g. white canes). Assistance systems capture information from the environment, process sensor data and provide the results to the impaired user. The paper presents an approach for efficient segmentation of obstacles in low-structured outdoor environments using encoder-decoder deep learning architectures and depth images. Therefore, an efficient method for generating training data using the v-disparity method is presented. Based on an extensive dataset of RGB and depth images and the corresponding binary label images, different state-of-the-art encoder-decoder architectures are evaluated on a mobile computing unit with respect to accuracy and efficiency. Besides pure depth-based architectures, RGB-D fused architectures are evaluated, too. The quantitative results show some limitations, but an additional qualitative evaluation proves the applicability of the approach to support the navigation of VIP by mapping the position of surrounding obstacles. Thus, an efficient combination of classical image processing, the integration of knowledge about the physical nature of the environment and deep learning can be made. Clinical Relevance- The approach supports the navigation of visually impaired people, which enables a more self-sufficient life related to higher quality of life.

A Novel Marker Detection System for People with Visual Impairment Using the Improved Tiny-YOLOv3 Model

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.

Special Issue on “Recent Advances in Indoor Localization Systems and Technologies”

Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this special issue present recent advances and new developments in indoor localization systems and technologies, proposing novel or improved methods with increased performance, providing insight into various aspects of quality control, and also introducing some unorthodox positioning methods.

Towards a Training Framework for Improved Assistive Mobile App Acceptance and Use Rates by Blind and Visually Impaired People

The development of mobile apps, which are either suitably adapted or especially designed for use by sensory-deprived people, have contributed significantly to the continuously increasing adoption of digital assistive technologies by people with disabilities. Throughout the design of two assistive navigation mobile apps for blind and visually impaired people (BVI), a set of everyday practices and psychological features of the BVIs with respect to the use of mobile technology was identified. Specifically, interviews with BVIs were held at the first stage of the design process. The analysis of the responses revealed that appropriate training of a BVI on how to use these apps plays significant role on the anticipated app adoption and use rate. This study presents the everyday practices and psychological features of the BVIs, as they were inferred from the analysis of the interviews. It is argued that these psychological features and practices must be considered in the development of training practices concerning the use of the proposed technology. Towards this direction, a framework for the adequate training of BVIs on the use of assistive mobile apps is presented. Consideration of this framework during the development of assistive mobile apps for BVIs could contribute towards higher adoption rates.