This survey provides a comprehensive overview of deep learning-based object detection, focusing on the development and advancements in the field. It begins by analyzing existing detection models and benchmark datasets, then systematically reviews various object detection methods, including one-stage and two-stage detectors. The survey also discusses traditional and new applications, key branches of object detection, and the architecture for building effective systems. It highlights recent contributions and emerging trends in the field, emphasizing the importance of backbone networks, typical baselines, and datasets like PASCAL VOC, MS COCO, ImageNet, VisDrone2018, and Open Images V5. The survey aims to provide an in-depth analysis of the latest advancements and future research directions in object detection.This survey provides a comprehensive overview of deep learning-based object detection, focusing on the development and advancements in the field. It begins by analyzing existing detection models and benchmark datasets, then systematically reviews various object detection methods, including one-stage and two-stage detectors. The survey also discusses traditional and new applications, key branches of object detection, and the architecture for building effective systems. It highlights recent contributions and emerging trends in the field, emphasizing the importance of backbone networks, typical baselines, and datasets like PASCAL VOC, MS COCO, ImageNet, VisDrone2018, and Open Images V5. The survey aims to provide an in-depth analysis of the latest advancements and future research directions in object detection.