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

doi:10.13466/j.cnki.lyzygl.2022.03.021

Abstract

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

CLC Number:

S763
S771.8

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, (3): 135-141.

Figures/Tables 4

References 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