A survey of object detection based on deep learning

Identifying and pinpointing objects within images or videos is a core objective in the field of computer vision, known as object detection. This study delves into the most recent advancements in object detection, with an emphasis on four primary approaches: Two-Stage Detectors, One-Stage Detectors, Anchor-Free Detectors, and Transformer-based Detectors. Each approach possesses distinct strengths and weaknesses, and the selection is dictated by the specific needs of the application. As research progresses, the techniques for object detection are advancing in precision and efficiency, while also expanding their ability to manage a wide array of object categories and scenarios. These advancements are pivotal in numerous domains, such as autonomous vehicles, security monitoring, and medical diagnostics.