Research on Crown Extraction Based on Improved Faster R-CNN Model

doi:10.13466/j.cnki.lyzygl.2021.01.022

Abstract

Abstract:

Canopy information is an important part of forest resources investigation.The traditional method of crown width measurement is through field survey,which may result in a significant error in the specific terrain and forest environment,along with great labor force,cumbersome and time-consuming operation procedure.The development of UAV imaging technology and machine learning provides a new method and realization idea for crown measurement.This paper employed UAV to obtain the orthophoto images of two pure Metasequoia glyptostroboides in the greenway of Qingshanhu in the east of Lin'an District.An advanced object detection method,Faster R-CNN,was improved to recognize the tree crown and extract the crown width.The Accuracy and R ² of the improved Faster R-CNN model are 92.92% and 0.84 respectively,which are 5.31% and 0.12 higher than those of the original model.This shows that the UAV and object detection technology are feasible to identify the tree crown.Compared with the traditional survey method,it has the advantages of high efficiency,convenience and low cost.

Key words: UAV images, crown recognition, crown width measurement, object detection, Faster R-CNN

CLC Number:

S758
TP751

HUANG Yanxiao, FANG Luming, HUANG Siqi, GAO Haili, YANG Laibang, LOU Xiongwei. Research on Crown Extraction Based on Improved Faster R-CNN Model[J]. FOREST RESOURCES WANAGEMENT, 2021, 0(1): 173-179.

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References 19

[1]	郭孝玉. 长白落叶松人工林树冠结构及生长模型研究[D]. 北京:北京林业大学, 2013.
[2]	Lee H, Slatton K C, Roth B E, et al. Adaptive clustering of airborne LiDAR data to segment individual tree crowns in managed pine forests[J]. International Journal of Remote Sensing, 2010,31(1):117-139.
[3]	李晓靖. 基于高分影像的面向对象分类与单木树冠提取研究[D]. 北京:北京林业大学, 2017.
[4]	刘玉锋, 潘英, 李虎. 基于高空间分辨率遥感数据的天山云杉树冠信息提取研究[J]. 国土资源遥感, 2019,31(4):112-119.
[5]	李永亮, 张怀清, 杨廷栋, 等. 基于自适应神经模糊系统的杉木冠幅估算方法[J]. 林业科学, 2019,55(11):45-51.
[6]	Erfanifard Y, Behnia N, Moosavi V. Tree crown delineation on UltraCam-D aerial imagery with SVM classification technique optimised by Taguchi method in Zagros woodlands[J]. International Journal of Image and Data Fusion, 2014,5(4):300-314.
[7]	Hearst M A, Dumais S T, Osuna E, et al. Support vector machines[J]. IEEE Intelligent Systems and their applications, 1998,13(4):18-28.
[8]	Wu Bin, Yu Bailang, Wu Qiusheng, et al. Individual tree crown delineation using localized contour tree method and airborne LiDAR data in coniferous forests[J]. International Journal of Applied Earth Observation and Geo Information, 2016,52:82-94.
[9]	Lecun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015,521(7553):436-444. doi: 10.1038/nature14539 pmid: 26017442
[10]	Ren S, He K, Girshick R, et al. Faster R-CNN:Towards real-time object detection with region proposal networks[J] //IEEE Trans Pattern Anal Mach Intell. 2017,39(6):1137-1149. pmid: 27295650
[11]	Everingham M, Van Gool L, Williams C K I, et al. The pascal visual object classes(voc)challenge[J]. International Journal of Computer Vision, 2010,88(2):303-338.
[12]	Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation [C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2014: 580-587.
[13]	Girshick R. Fast R-CNN [C]//Proceedings of the IEEE international conference on computer vision. 2015: 1440-1448.
[14]	Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J]. arXiv preprint arXiv:2014,1409:1556.
[15]	He Kaiming, Zhang Xiangyu, Ren Shaoqing, et al. Deep residual learning for image recognition [C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770-778.
[16]	Lin T Y, Dollár P, Girshick R, et al. Feature pyramid networks for object detection [C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 2117-2125.
[17]	Perez L, Wang J. The effectiveness of data augmentation in image classification using deep learning[J]. arXiv preprint arXiv:2017,1712:04621.
[18]	Deng Jia, Dong Wei, Socher R, et al. Imagenet:A large-scale hierarchical image database [C]//2009 IEEE conference on computer vision and pattern recognition.Ieee, 2009: 248-255.
[19]	Abadi M, Barham P, Chen J, et al. Tensorflow:A system for large-scale machine learning [C]//OSDI'16 Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation. 2016: 265-283.

指标	Faster R-CNN	Faster R-CNN_Res101_FPN
精确率/%	99	99.06
召回率/%	88.39	93.75
准确率/%	87.61	92.92
均方根误差	0.46	0.36
平均绝对误差	0.31	0.27
决定系数	0.72	0.84