Advanced Network Threat Detection Using Deep Learning Models

Object detection and recognition are important tasks in computer vision and are widely used in many fields. This article provides an overview and comparison of the main technologies used in the mission to detect, recognize and follow the changes transferred from modern methods to modern deep learning, starting from the historical background. This study compares and contrasts popular techniques such as field detectors, single-shot devices, and two-phase detectors, highlighting their advantages and disadvantages. In addition, recent developments are discussed, including one-off studies of monitoring systems and reform strategies, combining existing research and analysis control examples. This article is a useful resource for researchers to research and search for information.