Hybrid Optimization-Enabled Deep Learning for Indoor Object Detection and Distance Estimation to Assist Visually Impaired Persons

Abstract

Indoor object recognition mainly deals with recognizing indoor objects and in recent years, it becomes a crucial research topic among investigators. Visually Impaired Persons (VIPs) is a group of people who have a disability to visualize objects and indoor assistance to such people for safe navigation seems to be a challenging task. Several innovative devices and methods have been developed to assist VIP's towards their destination but, most of them failed to address the problem of multi-class object recognition. Moreover, such methods are incapable to provide an accurate distance between an object and a person. To bridge this gap, an efficacious model is proposed to assist VIP's in their daily lives for indoor object recognition using a newly designed Honey Adam African Vultures Optimization (HAAVO) algorithm. Here, object detection and object recognition is carried out using Generative Adversarial Network (GAN) and Deep Convolutional Neural Network (DCNN), respectively. The DCNN classifier and Deep Residual Network (DRN) utilized to estimate the distance is optimally trained using the proposed HAAVO. In addition, the designed HAAVO is obtained by integration of the Honey Badger Algorithm (HBA) with Adam Optimizer and African Vultures Optimization (AVO). The devised model provided high accuracy with respect to object detection and delivered superior performance values in accordance with testing accuracy, precision, and recall with the measures of 0.940, 0.946, and 0.953.