Enhancing Surveillance Systems with YOLO Algorithm for Real-Time Object Detection and Tracking

A Virtually Impaired Person (VIP) is unable to identify objects when they cannot recognize where the object is placed. The researchers are working on it to enhance object detection and help VIP. The challenges faced by researchers are performing detection under low-resolution images, insufficient sensors, portability, and cost. Making a compact device and alerting them is required. By considering the above-mentioned difficulties, an innovative solution is described in this research work. The growth of image processing and deep learning techniques has simplified the complexity of processing data and provided accurate results within a limited time period. The suggested technique presented is a deep learning algorithm called the YOLO algorithm, which is combined with the web to predict objects accurately. For this approach, a dataset with a total of 500 images was chosen and trained. The proposed classifier result is satisfactory, and it achieved an overall accuracy of 94% Furthermore, this proposed technique provides enough output in comparison with several other machine learning and image processing algorithms.