基于深度学习方法的YOLO目标检测综述

Overview of Yolo Object Detection Based on Deep Learning Methods

目标检测是计算机视觉中的一项重要任务,其目标是从图像或视频中检测出并定位感兴趣的目标对象。这与图像分类不同,目标检测不仅需要确定图像中是否存在特定对象,还需要准确地标定对象的位置。YOLO(You Only Look Once)是将目标检测问题转化为一个回归问题,从而实现了从端到端的一种检测方法。与传统的两阶段的目标检测算法相比,单阶段的目标检测算法在速度上有很大的提升,从而实现的速度与准确性的平衡。该文主要是对YOLO系列算法的网络结构以及相关改进进行了详细的阐述。首先是对YOLO算法基本思想进行相关阐述,然后对YOLO中相关网络架构进行的相关阐述,包括YOLO V1,YOLO V2,YOLO V3,YOLO V4,YOLO V5,以及YOLOX YOLO V7,YOLO V8。

Object detection is a critical task in computer vision,aiming to detect and locate objects of interest in images or videos.Unlike image classification,objcct detection not only requires determining the presence of specific objects in an image but also accurately localizing their positions.YOLO(You Only Look Once)transforms the object detection problem into a regression problem,thus providing an end-to-end detection approach.In comparison to traditional two-stage objcct detection algorithms,single-stage object detection algorithms significantly enhance speed,achieving a balance between speed and accuracy.This paper provides a detailed exposition of the network architectures of the YOLO series algorithms and their relevant improvements.It begins with an explanation of the fundamental ideas behind the YOLO algorithm,followed by discussions on the network architectures employed in YOLO,encompassing YOLO V1,YOLO V2,YOLO V3,YOLO V4,YOLO V5,as well as YOLOX YOLO V7,and YOLO V8.