Abstract: After the R-CNN framework is proposed, the object detection framework based on deep learning has gradually become the mainstream, which can be divided into one-stage and two-stage. In the past two years, based on the classic deep learning object detection frameworks such as Faster R-CNN, YOLO, and SSD, a large number of excellent frameworks have emerged. Firstly, according to the optimization method, the frameworks proposed in the past few years are sorted out and summarized. Then, the performance of the object detection methods is compared on the mainstream test sets such as PASCAL_VOC and MS COCO. The advantages and disadvantages are analyzed. Finally, the current difficulties and challenges in the field are discussed, and the possible development directions are prospected.

Key words: deep learning, object detection, convolutional neural networks, computer vision

摘要： 在R-CNN框架提出后，基于深度学习的目标检测框架逐渐成为主流，可分为基于候选窗口和基于回归两类。近两年来，在Faster R-CNN、YOLO、SSD等经典的基于深度学习目标检测框架的基础上，出现了大量的优秀框架。根据优化方法对近几年提出的框架进行了梳理和总结。在PASCAL_VOC和MS COCO等主流测试集上对目标检测方法的性能及优缺点进行了对比分析。讨论了目标检测领域当前面临的困难与挑战，对可能的发展方向进行了展望。

关键词: 深度学习, 目标检测, 卷积神经网络, 计算机视觉