|Table of Contents|

Object Detection to the Pine Trees Affected by Pine Wilt Diseasein Remote Sensing Images Using Deep Learning(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2021年03期
Page:
84-89
Research Field:
·地理学·
Publishing date:

Info

Title:
Object Detection to the Pine Trees Affected by Pine Wilt Diseasein Remote Sensing Images Using Deep Learning
Author(s):
Wang Chen; Zhang Huihui; Le Jiwang; Zhao Shuai
Yiwu Surveying and Design Institute,Yiwu 322000,China
Keywords:
pine wilt disease; remote sensing images; deep learning; YOLOv3; clustering statistics; NMS algorithm
PACS:
P208,P237
DOI:
10.3969/j.issn.1001-4616.2021.03.013
Abstract:
Aiming at the problems of high cost and low efficiency of the traditional large-scale monitoring method for pine wilt disease,the study is based on high resolution UAV remote sensing images and deep learning methods for target detection and geographic location of diseased pine trees in the epidemic area. Establish a sample data set of diseased pine trees,use the YOLO target detection model,and perform sample training through methods such as anchor frame size recalculation and model migration learning. Divide large images into sliding window segmentation and detect them one by one,after NMS overlap processing,coordinate conversion,the study gets the exact number and coordinates of deseased pine trees in the study area. The result shows that the model to detecting the deseased pine trees achieves a high level of 84.8% of precision rate and 81.7% of recall rate. Compared with the traditional visual interpretation method,the accuracy of this research algorithm is close,but the time-consuming is only 1/4 of the visual interpretation method. It can better meet the requirements of management departments for large-scale,high-precision,and rapid identification of pine wilt disease prevention and control.

References:

[1] 张田. 福建省三明市松材线虫病遥感监测预测研究[D]. 北京:北京林业大学,2010.
[2]魏健. 武夷山风景名胜区松材线虫监测技术与防范研究[D]. 福州:福建农林大学,2010.
[3]宁眺,方宇凌,汤坚,等. 松材线虫及其传媒松墨天牛的监测和防治现状[J]. 昆虫知识,2005,42(3):264-269.
[4]邓世晴. 星机地协同的松材线虫病疫区枯死松树监测方法研究[D]. 南昌:东华理工大学,2019.
[5]石进,马盛安,蒋丽雅,等. 航空遥感技术监测松材线虫病的应用[J]. 中国森林病虫,2006,25(1):18-20.
[6]王震,张晓丽,安树杰. 松材线虫病危害的马尾松林木光谱特征分析[J]. 遥感技术与应用,2007,22(3):367-370.
[7]黄宝华. 无人机搭载多光谱相机监测松材线虫病的研究[J]. 广西林业科学,2020,49(3):380-384.
[8]刘遐龄,程多祥,李涛,等. 无人机遥感影像的松材线虫病危害木自动监测技术初探[J]. 中国森林病虫,2019,37(5):16-21.
[9]尹宝才,王文通,王立春. 深度学习研究综述[J]. 北京工业大学学报,2015,41(1):48-59.
[10]CHOLLET F. Deep learning with python[M]. New York:Manning Publications,2017:6-8.
[11]龚健雅. 人工智能时代测绘遥感技术的发展机遇与挑战[J]. 武汉大学学报(信息科学版),2018,43(12):1788-1796.
[12]龚健雅,季顺平. 从摄影测量到计算机视觉[J]. 武汉大学学报(信息科学版),2017,42(11):21-25,118.
[13]张慧,王坤峰,王飞跃. 深度学习在目标视觉检测中的应用进展与展望[J]. 自动化学报,2017,43(8):1289-1305.
[14]朱润生. 基于深度学习的目标检测研究[D]. 北京:北京交通大学,2018.
[15]李旭冬,叶茂,李涛. 基于卷积神经网络的目标检测研究综述[J]. 计算机应用研究,2017,34(10):2881-2886.
[16]REDMON J, FARHADI A. YOLOv3:an incremental improvement[EB/OL]. [2018-04-08]. http://arxiv.org/abs/1804.02767.
[17]AMMAR A,KOUBAA A,AHMED M,et al. Aerial images processing for car detection using convolutional neural networks: comparison between faster R-CNN and YOLOv3[EB/OL]. [2020-04-22]. http://arxiv.org/abs/1910.07234.
[18]李淑敏,冯权泷,梁其椿,等. 基于深度学习的国产高分遥感影像飞机目标自动检测[J]. 遥感技术与应用,2018,33(6):1095-1102.
[19]任嘉锋,熊卫华,吴之昊,等. 基于改进YOLOv3的火灾检测与识别[J]. 计算机系统应用,2019,28(12):175-180.
[20]刘博,王胜正,赵建森,等. 基于Darknet网络和YOLOv3算法的船舶跟踪识别[J]. 计算机应用,2019,39(6):1663-1668.
[21]REDMON J,DIVVALA S,GIRSHICK R,et al. You only look once:unified,real-time object detection[EB/OL]. [2016-05-09]. http://arxiv.org/abs/1506.02640.
[22]REDMON J,FARHADI A. YOLO9000:better,faster,stronger[EB/OL]. [2016-12-25]. http://arxiv.org/abs/1612.08242.
[23]张希盛. 义乌市植物志[M]. 北京:中国林业出版社,2010.

Memo

Memo:
-
Last Update: 2021-09-15