基于深度学习的YOLO目标检测综述

目标检测是计算机视觉领域的一个基础任务和研究热点。YOLO将目标检测概括为一个回归问题，实现端到端的训练和检测，由于其良好的速度-精度平衡，近几年一直处于目标检测领域的领先地位，被成功地研究、改进和应用到众多不同领域。该文对YOLO系列算法及其重要改进、应用进行了详细调研。首先，系统地梳理了YOLO家族及重要改进，包含YOLOv1-v4, YOLOv5, Scaled-YOLOv4, YOLOR和最新的YOLOX。然后，对YOLO中重要的基础网络，损失函数进行了详细的分析和总结。其次，依据不同的改进思路或应用场景对YOLO算法进行了系统的分类归纳。例如，注意力机制、3D、航拍场景、边缘计算等。最后，总结了YOLO的特点，并结合最新的文献分析可能的改进思路和研究趋势。

A Review of YOLO Object Detection Based on Deep Learning

Object detection is one of the basic tasks and research hotspots in the field of computer vision. The YOLO (You Only Look Once) frames object detection is a regression problem to implement end-to-end training and detection. YOLO becomes the leading object detector due to its good speed-accuracy balance, which has been successfully studied, improved, and applied to many different fields. YOLO series and its important improvements and applications are investigated in detail. Firstly, the YOLO family and important improvements are systematically summarized, including YOLOv1-v4, YOLOv5, Scaled-YOLOv4, YOLOR, and the latest YOLOX. Then, important backbone and loss functions in YOLO are analyzed and summarized in detail. Next, the application of YOLO is systematically classified and summarized according to different improvement ideas or scenarios, such as attention mechanisms, three-dimensional scenes, aerial scenes, edge computing, etc. Finally, the characteristics of the YOLO series are summarized and the possible improvement ideas and research trends are analyzed in combination with the latest literature.