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Urban forest individual tree crown detection method combining RGB-DSM image and deep learning

A deep learning and detection method technology, applied in the field of target detection, to achieve the effect of improving performance

Active Publication Date: 2021-11-02
ZHEJIANG FORESTRY UNIVERSITY
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

Some researchers have used elevation data in ITCD research, but these studies have used elevation data in ITCD tasks based on non-deep learning algorithms, but since the color image and elevation data are three-channel images and single-channel images respectively (the total of the two There are four channels), while the current general-purpose deep learning network accepts three-channel images, so before combining two types of data for deep learning-based ITCD tasks, we must try to solve the contradiction between the number of channels between the research data and the network

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  • Urban forest individual tree crown detection method combining RGB-DSM image and deep learning

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Embodiment

[0043] In this embodiment, the implementation process of the single-tree canopy detection method in the urban forest combined with RGB-DSM images and deep learning is as follows:

[0044] 1. UAV image data collection

[0045] The surrounding area of ​​the campus of Zhejiang A&F University located in Lin'an District, Hangzhou City, Zhejiang Province is used as the target detection area, and the representative camphor tree in this area is used as the detection object.

[0046] In this embodiment, the Phantom 4 RTK series UAV of Dajiang Innovation Technology Co., Ltd. and its built-in camera are used as the image acquisition system. The detailed parameters of the UAV image acquisition system are shown in Table 1. The images were acquired from March 3, 2021 to March 15, 2021, in a sunny or cloudy environment with low wind speed, using the method of automatic cruise and timing shooting of drones. Present embodiment carries out flight task 31 times altogether, and flight parameter ...

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Abstract

The invention discloses an urban forest individual tree crown detection method combining RGB-DSM images and deep learning, and belongs to the field of target detection. According to the urban forest individual tree crown detection method combining two images and deep learning, a model firstly combines elevation information and color information to carry out individual tree crown target detection, and then a new detection result is formed through further confidence coefficient resetting and redundancy elimination. Therefore, the performance of an urban forest single tree crown detection task is improved. Compared with a deep learning network trained only by using an orthophoto map or a digital surface model, the method provided by the invention has an obvious precision advantage.

Description

technical field [0001] The invention belongs to the field of target detection, and in particular relates to a method for detecting the crown of a single tree in an urban forest combined with RGB-DSM images and deep learning. Background technique [0002] Urban forests play an important role in regulating urban climate, absorbing toxic and harmful gases, improving living environment and maintaining biodiversity. With the increasingly serious effect of urbanization, the construction of urban forests has been paid more and more attention. Individual tree crown detection (ITCD) is an important method to achieve sustainable management and management of urban forests. It is not only used to obtain basic information of trees, but also has important applications in diseased tree detection and urban green quantity monitoring. . [0003] The ITCD task can be divided into two steps: position detection and edge delineation. Commonly used algorithms for position detection include loca...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/32G06K9/46G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/214Y02T10/40
Inventor 夏凯王昊冯海林杨垠晖徐流畅
Owner ZHEJIANG FORESTRY UNIVERSITY
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