基于改进SSD的无人机影像松材线虫病变色木检测

doi:10.13466/j.cnki.lyzygl.2022.03.021

林业资源管理 ›› 2022, Vol. 0 ›› Issue (3): 135-141.doi: 10.13466/j.cnki.lyzygl.2022.03.021

基于改进SSD的无人机影像松材线虫病变色木检测

刘顺利¹(), 刘昌华¹(), 张雷¹, 彭词清², 薛冬冬²

1.河南理工大学测绘与国土信息工程学院,河南焦作 454150
2.广东省岭南综合勘察设计院,广州 510700

收稿日期:2022-04-19 修回日期:2022-05-02 出版日期:2022-06-28 发布日期:2022-08-04
通讯作者: 刘昌华
作者简介:刘顺利(1994-),男,河南太康人,在读硕士,主要研究方向:摄影测量与遥感技术应用。Email: 2320926733@qq.com
基金资助:
国家自然科学基金资助项目(41541014);河南省高等学校重点科研项目(18A420001)

Detection of Pine Trees Infected by Pine Wood Nematode with UAV Images Based on Improved SSD

LIU Shunli¹(), LIU Changhua¹(), ZHANG Lei¹, PENG Ciqing², XUE Dongdong²

1. School of Surveying and Land Information Engineering,Henan Polytechnic University,Jiaozuo,Henan 454150,China
2.Guangdong Lingnan Comprehensive Survey and Design Institute,Guangzhou 510700,China

Received:2022-04-19 Revised:2022-05-02 Online:2022-06-28 Published:2022-08-04
Contact: LIU Changhua

摘要/Abstract

摘要：

快速精确获取松材线虫病变色木的株数和位置信息,结合相应的治理措施是防止松材线虫病进一步蔓延的有效手段。通过无人机获取松材线虫病变色木林区的厘米级影像,采用SSD,YOLO v4和Faster R-CNN三种深度学习算法实现对变色木的自动检测。结果表明:相比YOLO v4和Faster R-CNN,SSD对于变色木的总体精度更高为75.0%;提出一种结合特征融合模块和通道注意力机制模块的方法改进SSD,改进SSD的总体检测精度为79.0%,相比SSD总体检测精度提升4.0%,表明改进SSD比SSD更适合变色木检测。验证区的变色木株数为87株,改进SSD正确检测株数为81株,检测的正确率高达93.1%,实现对林区无人机影像中松材线虫病变色木的精准检测,可为松材线虫病变色木的防治工作提供技术支持。

关键词: 松材线虫病, 特征融合, 通道注意力模块, 改进SSD

Abstract:

It is an effective means to prevent the further spread of pine wilt disease by quickly and accurately obtaining the number and location information of pine wilt discoloration wood and combining with the corresponding control measures.The centimeter-level image of pine wilted wood forest area was obtained by UAV,and the automatic detection of discoloration wood was realized by SSD,YOLO v4 and Faster R-CNN.The experimental results showed that compared with YOLO v4 and Faster R-CNN,the overall accuracy of SSD for discoloration wood was 75.0%.A method combining feature fusion module and channel attention mechanism module was proposed to improve SSD.The overall detection accuracy of improved SSD was 79.0%,which was 4.0% higher than that of SSD,indicating that the improved SSD was more suitable for discoloration wood detection than SSD.In addition,the number of discoloration trees in the verification area was 87,and the number of correct detection trees in the improved SSD was 81.The accuracy of detection was as high as 93.1%.The accurate detection of pine wilt discoloration trees in UAV images of forest areas was realized,which can provide technical support for the prevention and control of pine wilt discoloration trees.

Key words: Bursaphelenchus xylophilus, feature fusion, squeeze and excitation block, improved SSD

中图分类号:

S763
S771.8

刘顺利, 刘昌华, 张雷, 彭词清, 薛冬冬. 基于改进SSD的无人机影像松材线虫病变色木检测[J]. 林业资源管理, 2022, 0(3): 135-141.

LIU Shunli, LIU Changhua, ZHANG Lei, PENG Ciqing, XUE Dongdong. Detection of Pine Trees Infected by Pine Wood Nematode with UAV Images Based on Improved SSD[J]. FOREST RESOURCES WANAGEMENT, 2022, 0(3): 135-141.

图/表 4

参考文献 21

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算法	IOU阈值	精确率/%	召回率/%	平均精度/%	总体精度/%	每秒检测图片数量/s
YOLO v4	0.50	90.53	60.78	77.44	73.11	39.68
Faster R-CNN	0.50	60.11	76.68	73.98	67.23	6.37
SSD	0.50	78.86	71.85	79.43	75.00	57.89
改进SSD	0.50	86.09	73.33	80.40	79.00	40.81

基于改进SSD的无人机影像松材线虫病变色木检测

Detection of Pine Trees Infected by Pine Wood Nematode with UAV Images Based on Improved SSD

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参考文献 21

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算法	真实株数/株	检测株数/株	误检株数/株	正确检测株数/株	准确率/%
YOLO v4	87	61	2	59	67.8
Faster R-CNN	87	85	15	70	80.5
SSD	87	81	10	71	81.6
改进SSD	87	87	6	81	93.1

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