• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Conference on Artificial Intelligence and Security >Survey on Image Object Detection Algorithms Based on Deep Learning
【24h】

Survey on Image Object Detection Algorithms Based on Deep Learning

机译：基于深度学习的图像目标检测算法综述

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

With the development of image processing technology, computer vision is becoming more and more popular. In recent years, deep learning has flourished, significant progress has been made in object detection. Especially after the R-CNN framework was proposed, the object detection framework based on deep learning has gradually become the mainstream, which can be divided into two categories: region-based and regression-based. Taking these two types of frameworks as the main body, this paper summarizes the research background and then discusses the object detection algorithms based on candidate regions represented by Faster R-CNN and the algorithms based on regression represented by the YOLO series. According to the development history, this paper summarizes the framework proposed in recent years, compares and analyzes the performance of object detection algorithms on public datasets, and introduces the application scenarios of those algorithms. Finally, this paper discusses the current difficulties and challenges in object detection and looks forward to the future development direction.
机译：随着图像处理技术的发展，计算机视觉越来越普及。近年来，深度学习蓬勃发展，在目标检测方面取得了重大进展。尤其是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，可分为基于区域和基于回归的两类。本文以这两类框架为主体，总结了研究背景，讨论了基于快速R-CNN表示的候选区域的目标检测算法和基于YOLO级数表示的回归算法。根据发展历史，本文总结了近年来提出的框架，比较分析了目标检测算法在公共数据集上的性能，并介绍了这些算法的应用场景。最后，本文讨论了当前目标检测面临的困难和挑战，并展望了未来的发展方向。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号