无人机松林图像早期松材线虫病害检测

doi:10.11707/j.1001-7488.LYKX20220898

Abstract

Abstract:

Objecive: Aiming at the problem of missed and misdetection of targets due to the inconspicuous features and variable scales of early pine wood nematode infestation in UAV (unmanned aerial vehicle) forest images, this paper proposes a detection method for early pine wood nematode infestation. Method: Firstly, in order to facilitate the deployment at the airborne end of the UAV, this paper proposes a pine wood nematode infestation detection method that reduces the amount of computation and the number of parameters; secondly, in order to obtain more accurate features of early pine wood nematode infestation, the method uses multiple ways of simultaneous extraction of features and fusion to enhance the learning ability of effective features; then, in order to further improve the fusion ability of features at different scales, the method uses the attention mechanism to Aligning the adjacent two levels of features; finally, taking the early pine nematode infestation photographed by UAV in Dahuofang experimental forest in Fushun, Liaoning Province as the object of study, the LablImg open source software was used to annotate the images captured at the heights of 100?240 m, and to construct the early pine nematode infestation images (EPI) (early pine wood nematode infestation) dataset. Results: The test results on the EPI dataset showed that the Average Precision (AP) of this paper's method reached up to 95.2%, which was improved by 3.1%, 4% and 0.9% compared with the AP of YOLOv5s, YOLOX-s and YOLOv6s, respectively; the volume of the model was only 12.8 M, which was the volume of YOLOv5s, YOLOx-s and YOLOv6s, respectively, 23.6%, 17.8% and 8.9% of the model volume of YOLOX-s and YOLOv6s, respectively. Conclusion: In summary, the method in this paper has high recognition accuracy while keeping the model volume small, which provides the possibility for UAVs to recognize early pine wood nematode infestation at the airborne end.

Key words: unmanned aerial vehicle imagery, pine wilt disease, deep learning, object detection

CLC Number:

TP391

Wanying Xie,Wenping Liu,Han Wang. UAV Images of Pine Forests for Early Detection of Pine Wood Nematode Infestation[J]. Scientia Silvae Sinicae, 2024, 60(9): 124-133.

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References 0

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方法 Method	F(%)	AP0.5(%)	AP0.5:0.95(%)
+普通卷积块 Standard convolutional block +CSP	90.2	92.1	64.4
+普通卷积块 Standard convolutional block +MFEM3	90.3	93.4	67.6
+MFEB +CSP	90.3	93.6	67.5
+MFEB +MFEM3	91.0	94.1	67.8

方法 Method	F(%)	AP0.5(%)	AP0.5:0.95(%)
+FAFM	90.0	91.9	64.1
+FAFM +CA	91.7	94.2	66.4
+FAFM +SENet	90.9	93.1	65.9
+FAFM +CBAM	72.8	74.0	43.1

层 Layer	输入图像尺寸 Input image size/pixels	模块 Module	n	s
C1	800×800×3	DS-Focus	—	2
C2	400×400×32（80）	MFEB	—	2
C2	200×200×64（160）	MFEM3	1	1
C3	200×200×64（160）	MFEB	—	2
C3	100×100×128（320）	MFEM3	3	1
C4	100×100×128（320）	MFEB	—	2
C4	50×50×256（640）	MFEM3	3	1
C5	50×50×256（640）	MFEB	—	2
C5	25×25×512（1280）	SPPF	—	1
C5	25×25×512（1280）	MFEM3	1	1

方法 Method	DS	MFEB	MFEM3	FAFM+CA	PANet引入新特征 PANet introduces new features	F(%)	AP0.5(%)	AP0.5:0.95(%)	P/M	FLOPs/G
YOLOv5s						90.2	92.1	64.4	7.1	16.3
策略1 Strategy 1	√					88.6	93.2	61.2	5.2	10.8
策略2 Strategy 2		√	√			91.0	94.1	67.8	4.6	13.4
策略3 Strategy 3				√		91.7	94.2	66.4	9.8	19.1
策略4 Strategy 4					√	90.5	92.5	64.9	7.3	17.4
策略5 Strategy5	√	√				91.3	94.5	66.9	5.5	15.3
策略6 Strategy 6	√	√	√			92.1	94.7	66.8	4.8	11.1
策略7 Strategy7	√	√	√	√		91.4	94.6	68.7	6.5	13.7
本研究方法-s Our method-s	√	√	√	√	√	93.1	95.2	68.8	6.57	13.9
YOLOv5m						91.4	92.4	68.5	21.3	50.2
策略8 Strategy 8	√					88.6	93.5	63.7	12.8	26.9
策略9 Strategy 9		√	√			91.8	94.7	70.2	13.6	36.8
策略10 Strategy10				√		91.2	94.3	69.0	26.1	55.8
策略11 Strategy11					√	90.9	92.9	68.8	15.5	52.1
策略12 Strategy12	√	√				91.6	94.4	68.1	13.5	40.2
策略13 Strategy13	√	√	√			92.9	94.9	68.4	9.9	28.4
策略14 Strategy 14	√	√	√	√		92.6	95.6	68.7	15.7	33.5
本研究方法-m Our method-m	√	√	√	√	√	93.4	96.2	69.5	15.8	33.9

方法 Method	主干网络 Backbone network	输入图像尺寸 Input image size/pixels	内存占用量 Memory usage/M	每秒处理图像的帧数 Frames per second (FPS)	AP0.5(%)
Faster R-CNN	VGG16	800×800	982.6	4.8	86.7
Faster R-CNN	ResNet101	800×800	377.5	3.6	87.9
Fcos	ResNet101	800×800	409.7	7.5	89.9
RetinaNet	ResNet101	640×640	808.2	5.8	88.7
SSD	VGG16	640×640	—	—	88.5
YOLOv5s	ResNet50	800×800	242.0	64.1	93.2
YOLOv5s	CSPDarkNet53	800×800	54.3	109.0	92.1
YOLOv5m	CSPDarkNet53	800×800	161	70.0	92.4
YOLOX-s	CSPDarkNet53	640×640	72.1	67.1	91.2
YOLOv6s	EfficientRep	800×800	144.0	85.5	94.3
本研究方法-s Our method-s	—	800×800	12.8	55.3	95.2
本研究方法-m Our method-m	—	800×800	30.6	38.0	96.2

UAV Images of Pine Forests for Early Detection of Pine Wood Nematode Infestation

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