Real-time Object Detection: A Deep Dive into YOLOv2 and Contemporary Algorithms

Abstract

Object detection is an integral part of computer vision, a subject of extensive research and development over the past few decades. Real-time object detection in a range of complex environments is challenging, especially for applications that need fast reactions, such real-time surveillance or autonomous driving. Traditional object identification methods are vital, but they usually fall short in dynamic environments, demanding a compromise between detection accuracy and processing speed. The object recognition system developed in this study, effectively combines the robustness of deep learning features with accuracy of excellent object location predictions. Our method tries to capture subtle patterns and features that frequently missed by conventional methods by utilizing the representational capabilities of deep neural networks. When combined with our improved object localization method, this deep feature extraction ensures precise bounding box predictions, which greatly reduces false positives and enhances the granularity of detection. Benefits of our approach in terms of detection accuracy, speed, and dependability have also been confirmed by early experiments carried out on benchmark datasets. These results demonstrate how our method may redefine the parameters of object detection, particularly in conditions when there are large numbers of overlapping objects. This study exhibits the potential of combining deep learning with quality-driven object localization, representing a substantial advance in constantly changing field of object recognition.