测绘通报 ›› 2020, Vol. 0 ›› Issue (2): 77-81,101.doi: 10.13474/j.cnki.11-2246.2020.0048

• 学术研究 • 上一篇    下一篇

分部加权的行道树检测网络构建

沈雨1, 仇宇轩1, 于正浩2   

  1. 1. 南京市测绘勘察研究院股份有限公司, 江苏 南京 210019;
    2. 南京航空航天大学, 江苏 南京 210016
  • 收稿日期:2019-11-05 修回日期:2019-12-17 出版日期:2020-02-25 发布日期:2020-03-04
  • 作者简介:沈雨(1977-),男,硕士,高级工程师,主要研究3S技术、WebGL技术、AI技术在城市排水、园林及综合市政领域精细化管理的深度应用。E-mail:njgis@qq.com
  • 基金资助:
    国家自然科学基金(61402224)

The construction of road tree detection network based on segment weighting

SHEN Yu1, QIU Yuxuan1, YU Zhenghao2   

  1. 1. Nanjing Institute of Surveying, Mapping&Geotechnical Investigation, Co., Ltd., Nanjing 210019, China;
    2. Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
  • Received:2019-11-05 Revised:2019-12-17 Online:2020-02-25 Published:2020-03-04

摘要: 基于深度学习目标检测框架,提出了一种端到端的训练网络,用于行道树的自动检测。由于行道树之间的遮挡问题,现有的通用物体检测框架无法直接应用于此任务,为此本文提出了一种树形分部加权模块,以减少严重遮挡造成的错误检测。然后对提出的神经网络进行训练和评估。结果显示,本文所建立的分部加权树木检测网络能够在遮挡条件下,有效地检测出街景图像中的行道树,该方法在各种条件下均具有较高的精度和良好的稳健性。

关键词: 行道树, 树形检测, 卷积神经网络, 深度学习, 目标检测

Abstract: In this paper, an end-to-end training network is proposed based on the framework of deep learning forobject detection, which can be used for automatic street treesdetection. Because of the occlusion problem between the roadway trees, the existing general object detection framework cannot be applied to this task directly. In this paper, a tree-shaped partial weighting module is proposed to reduce the error detection caused by severe occlusion. Then, the proposed neural network is trained and evaluated. The results show that the partitioned weighted tree detection network established in this paper can effectively detect the street trees in street scenes under occlusion conditions. The experimental results further show that the method gets high accuracy and good robustness under various conditions.

Key words: street tree, tree detection, convolutional neural network, deep learning, object detection

中图分类号: