基于深度学习的目标检测研究

Research on Object Detection Based on Deep Learning

目标检测是机器视觉领域的核心问题。经过多年的发展,以深度学习技术为基础的目标检测方法已成为研究热点。根据检测的原理和过程,目标检测可分为一阶段目标检测和二阶段目标检测。首先,在进行广泛文献调研的基础上,对比目前主流目标检测方法的原理、思路,其次,使用mAP和FPS两个参数对比各方法的检测效果,分析常见目标检测方法的优缺点;最后,对目标检测的发展做出预测和展望。

Object detection is a core issue in the field of Machine Vision.After years of development,object detection methods based on Deep Learning technology have become a research hotspot.According to the principle and process of detection,object detection can be divided into one-stage object detection and two-stage object detection.Firstly,based on extensive literature research,the principles and ideas of mainstream object detection methods are compared.Then,the detection effects of various methods are compared using two parameters,the mAP and the FPS,and the advantages and disadvantages of common object detection methods are analyzed.Finally,predictions and prospects are made for the development of object detection.