A smart assistive system for visually challenged people through efficient object detection using deep learning with tunicate swarm algorithm

Visual impairment is an inability to see something to a level that the eye is unable to see, even with usual means, such as glasses or lenses. Therefore, satisfying the everyday tasks of life becomes very hard for them. To help them, having more accurate and comprehensive object detection becomes essential. In this regard, one cares not only about categorizing images but also about accurately assessing the position and class of objects within the images, which is recognized as object detection. Robust object detection enables reliable DL-based recognition of specific objects in indoor and outdoor scenes, assisting visually impaired users. This manuscript proposes a Smart Assistive System for Visually Challenged People through Object Detection Using the Tunicate Swarm Algorithm (SASVCP-ODTSA) method. The SASVCP-ODTSA method aims to detect objects using advanced techniques to assist individuals with disabilities automatically. To accomplish this, the image pre-processing phase utilizes median filtering (MF) to remove noise, thereby enhancing image quality. Furthermore, the YOLOV8 method is used for object detection. For the feature extraction process, the CapsNet model is employed. Moreover, the deep belief network (DBN) model is implemented for the detection and classification process. The classification performance of the deep belief network (DBN) is improved by optimizing its parameters using the tunicate swarm algorithm (TSA) model. The experimental evaluation of the SASVCP-ODTSA model is examined using the Indoor Object Detection dataset. The comparison analysis of the SASVCP-ODTSA approach revealed a superior accuracy value of 99.58% compared to existing models.