Deep Learning based Object Detection and Recognition Framework for the Visually-Impaired
Swapnil Bhole, Aniket Dhok
Abstract
Vision impairment or blindness is one of the top ten disabilities in humans, and unfortunately, India is home to the world's largest visually impaired population. In this study, we present a novel framework to assist the visually impaired in object detection and recognition, so that they can independently navigate, and be aware of their surroundings. The paper employs transfer learning on Single-Shot Detection (SSD) mechanism for object detection and classification, followed by recognition of human faces and currency notes, if detected, using Inception v3 model. SSD detector is trained on modified PASCAL VOC 2007 dataset, in which a new class is added, to enable the detection of currency as well. Furthermore, separate Inception v3 models are trained to recognize human faces and currency notes, thus making the framework scalable and adaptable according to the user preferences. Ultimately, the output from the framework can then be presented to the visually impaired person in audio format. Mean Accuracy and Precision (mAP) scores of standalone SSD detector of the added currency class was 67.8 percent, and testing accuracy of person and currency recognition of Inception v3 model were 92.5 and 90.2 percent respectively.