Overview of Yolo Object Detection Based on Deep Learning Methods
Object detection is a critical task in computer vision,aiming to detect and locate objects of interest in images or videos.Unlike image classification,object detection not only requires de-termining the presence of specific objects in an image but also accurately localizing their posi-tions.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 object 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 discus-sions 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.
Deep LearningConvolutional Neural NetworksObject DetectionYOLO
万方数据
维普
