A survey of deep learning-based object detection discusses the development and applications of object detection in computer vision. Object detection is crucial for tasks like security monitoring, autonomous driving, and scene understanding. Recent advances in deep learning have significantly improved detection performance. The paper analyzes existing detection methods, benchmark datasets, and provides a comprehensive overview of one-stage and two-stage detectors. It also covers traditional and new applications, and discusses system architecture and development trends. Two-stage detectors, such as Faster R-CNN, use region proposal networks to generate candidate boxes, while one-stage detectors, like YOLO and SSD, directly predict bounding boxes from input images. Two-stage detectors offer high accuracy but are slower, while one-stage detectors are faster but less accurate. The paper highlights the importance of backbone networks in feature extraction and discusses various architectures like ResNet, MobileNet, and Darknet. The paper reviews key object detection methods, including R-CNN, Fast R-CNN, Faster R-CNN, Mask R-CNN, YOLO, YOLOv2, YOLOv3, SSD, DSSD, RetinaNet, and M2Det. It also discusses recent advancements like Relation Networks, DCNv2, and NAS-FPN. The paper evaluates these methods on benchmark datasets such as PASCAL VOC, MS COCO, ImageNet, and VisDrone2018, highlighting their performance in terms of accuracy, speed, and handling of different object sizes and scales. The paper emphasizes the importance of feature extraction, multi-scale detection, and handling geometric variations. It also discusses the use of focal loss, deformable convolutions, and neural architecture search to improve detection performance. The survey concludes that deep learning-based object detection has made significant progress, with ongoing research focusing on improving accuracy, speed, and robustness in various applications.A survey of deep learning-based object detection discusses the development and applications of object detection in computer vision. Object detection is crucial for tasks like security monitoring, autonomous driving, and scene understanding. Recent advances in deep learning have significantly improved detection performance. The paper analyzes existing detection methods, benchmark datasets, and provides a comprehensive overview of one-stage and two-stage detectors. It also covers traditional and new applications, and discusses system architecture and development trends. Two-stage detectors, such as Faster R-CNN, use region proposal networks to generate candidate boxes, while one-stage detectors, like YOLO and SSD, directly predict bounding boxes from input images. Two-stage detectors offer high accuracy but are slower, while one-stage detectors are faster but less accurate. The paper highlights the importance of backbone networks in feature extraction and discusses various architectures like ResNet, MobileNet, and Darknet. The paper reviews key object detection methods, including R-CNN, Fast R-CNN, Faster R-CNN, Mask R-CNN, YOLO, YOLOv2, YOLOv3, SSD, DSSD, RetinaNet, and M2Det. It also discusses recent advancements like Relation Networks, DCNv2, and NAS-FPN. The paper evaluates these methods on benchmark datasets such as PASCAL VOC, MS COCO, ImageNet, and VisDrone2018, highlighting their performance in terms of accuracy, speed, and handling of different object sizes and scales. The paper emphasizes the importance of feature extraction, multi-scale detection, and handling geometric variations. It also discusses the use of focal loss, deformable convolutions, and neural architecture search to improve detection performance. The survey concludes that deep learning-based object detection has made significant progress, with ongoing research focusing on improving accuracy, speed, and robustness in various applications.