Obs-tackle: an obstacle detection system to assist navigation of visually impaired using smartphones

Abstract

As the prevalence of vision impairment continues to rise worldwide, there is an increasing need for affordable and accessible solutions that improve the daily experiences of individuals with vision impairment. The Visually Impaired (VI) are often prone to falls and injuries due to their inability to recognize dangers on the path while navigating. It is therefore crucial that they are aware of potential hazards in both known and unknown environments. Obstacle detection plays a key role in navigation assistance solutions for VI users. There has been a surge in experimentation on obstacle detection since the introduction of autonomous navigation in automobiles, robots, and drones. Previously, auditory, laser, and depth sensors dominated obstacle detection; however, advances in computer vision and deep learning have enabled it using simpler tools like smartphone cameras. While previous approaches to obstacle detection using estimated depth data have been effective, they suffer from limitations such as compromised accuracy when adapted for edge devices and the incapability to identify objects in the scene. To address these limitations, we propose an indoor and outdoor obstacle detection and identification technique that combines semantic segmentation with depth estimation data. We hypothesize that this combination of techniques will enhance obstacle detection and identification compared to using depth data alone. To evaluate the effectiveness of our proposed Obstacle detection method, we validated it against ground truth Obstacle data derived from the DIODE and NYU Depth v2 dataset. Our experimental results demonstrate that the proposed method achieves near 85% accuracy in detecting nearby obstacles with lower false positive and false negative rates. The demonstration of the proposed system deployed as an Android app-‘Obs-tackle’ is available at https://youtu.be/PSn-FEc5EQg?si=qPGB13tkYkD1kSOf.