Sine Cosine Optimization With Deep Learning–Driven Object Detector for Visually Impaired Persons

In real-time, visually impaired persons face challenging issues in identifying and avoiding obstacles in their path, making it difficult to move around freely and confidently. Object detection technology benefits visually impaired persons and contains the procedure of determining several individual objects from the image and recognizing their place with bounding boxes with class labels. Both exploit typical machine learning (ML) approaches and utilize deep learning (DL) models to execute object detection methods. Recently, several researchers have leveraged DL techniques such as convolutional neural networks (CNNs) for solving object detection problems and presented various recent object detection approaches. The study presents a sine cosine optimization with DL–driven object detector (SCODL–ODC) technique for visually impaired persons. The presented SCODL–ODC technique detects and classifies the objects, which can help them navigate their environment more efficiently and avoid obstacles. To accomplish this, the proposed SCODL–ODC technique employs a YOLOv5 object detector at the initial stage to detect the objects accurately, and the SCO model performs its hyperparameter tuning process. The following step is to classify them using the deep stacked autoencoder (DSAE) model when the objects are detected. Finally, the Harris Hawks optimization (HHO) technique is used to adjust the hyperparameters of the DSAE model. The experimentation outcome of the SCODL–ODC method is examined on the object detection dataset. The investigational validation of the SCODL–ODC method portrayed a superior accuracy value of 99.31% over existing models.