Stochastic Gradient Descent with Deep Learning-assisted Object Detection and Classification for Visually Challenged People

Abstract

Object detection and classification systems can be devised to support visually challenged persons in communicating and understanding their environments. Such systems use computer vision methods for classifying and detecting objects in real time. Deep learning (DL) can be adopted to help visually challenged persons in object classification and detection tasks, allowing them to communicate and understand their surroundings more efficiently. By leveraging DL for object detection and classification, visually challenged individuals can receive real-time data regarding their interaction, surroundings, and overall independence and facilitate their navigation. With this motivation, the study presents a novel Stochastic Gradient Descent with Deep Learning-assisted Object Detection and Classification (SGDDL-ODC) technique for visually challenged people. The main intention of the SGDDL-ODC technique concentrates on the accurate and automated detection of objects to help visually challenged people. To obtain this, the SGDDL-ODC technique focused on the development of the optimal hyperparameter tuning of the DL models effectively. To accomplish this, the SGDDL-ODC technique follows the YOLOv6 model for object detection purposes. To adjust the hyperparameter values of the YOLOv6 method, the SGD model can be applied. At the final stage, the deep neural network method can be exploited for the classification of the recognized objects. A series of simulations were performed to validate the improved performance of the SGDDL-ODC approach. The simulation results illustrate the superior efficiency of the SGDDL-ODC technique over other techniques under diverse datasets in terms of different measures.