Hybrid Optimization-Enabled Deep Learning for Indoor Object Detection and Distance Estimation to Assist Visually Impaired Persons. (February 2023) - Nagarajan, Anandh; M P, Gopinath
Highlights: The highlights of the article are given below for your kind perusal. Kindly, consider and forward my article for further processes. This research work is highly focused on indoor object detection. This research work is highly meaningful, as it helps visually impaired persons to detect indoor objects and also ensures safe navigation. Moreover, to know about the most interesting works undergone in literature, we have collected a total of 8 literature papers on indoor object detection concepts. All these papers were collected from 2019 to 2022 (i.e. 4 years). Analyzing the past as well as recent works helps to gain more knowledge regarding the indoor object detection. In addition, it's good to learn the advantage and drawbacks of existing works, which might be a mile stone for the future researchers. 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.