Deep Learning Approach for Multi-Object Detection Using Yolo Algorithm

Abstract

The fundamental task of object detection in computer vision has applications in many fields, including robots, surveillance systems, and autonomous vehicles. The You Only Look Once (YOLO) method has attracted substantial attention because of its real-time performance and high accuracy since the emergence of deep learning, which has revolutionized object detection techniques. This research study demonstrates a deep learning method for multi-object detection. The study assesses YOLO's performance in identifying multiple objects in cluttered and complicated settings and compares it to other cutting-edge object detection techniques. The effectiveness and efficiency of the suggested approach in attaining precise and real-time multi-object identification are demonstrated by experimental findings. This study also investigates the effects of various YOLO algorithm versions on multi-object identification performance, revealing the advantages and disadvantages of each. The results demonstrate the promise of deep learning-based methods, particularly the YOLO algorithm, for reliable and effective multi-object recognition in practical settings.