Detection of forest pests based on improved YOLOv4

doi:10.3969/j.issn.1004-1524.2022.06.21

Acta Agriculturae Zhejiangensis ›› 2022, Vol. 34 ›› Issue (6): 1306-1315.DOI: 10.3969/j.issn.1004-1524.2022.06.21

• Biosystms Engineening • Previous Articles Next Articles

Detection of forest pests based on improved YOLOv4

CHEN Daohuai¹(), WANG Hangjun²^,^*()

1. College of Information Engineering, Zhejiang A&F University, Hangzhou 311300, China
2. College of Engineering and Technology, Jiyang College of Zhejiang A&F University, Zhuji 311800, Zhejiang, China

Received:2021-07-03 Online:2022-06-25 Published:2022-06-30
Contact: WANG Hangjun

Abstract

Abstract:

Abstract：In order to improve the accuracy of pest detection in the forest, an improved algorithm based on YOLOv4 was proposed. First, based on the image captured by the intelligent pest capture device, the pest data set was made, and K-means algorithm was used to cluster the target frame of the sample data set. Based on the DIoU-NMS algorithm, the counting function of pests was realized. Then, feature fusion was added to the path aggregation network (PANet) structure of the model, as well as 104×104 hierarchical feature detection graph, to increase the recognition accuracy of small-sized pests. Finally, according to the efficiency and complexity of model detection, the combination of scale feature maps in the model was adjusted to ensure the detection accuracy, improve the detection efficiency and simplify the model. The experimental results showed that the mean average precision of the improved YOLOv4 model was 1.6 percent higher than that of the traditional YOLOv4 model with a better performance on the detection of small-sized pests. Besides, its speed was improved by 11.1 percent, and the model complexity and model parameters were reduced by 11.9 percent and 33.2 percent, respectively, as compared with the traditional YOLOv4 model, which was more suitable for application deployment.

Key words: forest pests, pest detection, deep learning

CLC Number:

S431.9

CHEN Daohuai, WANG Hangjun. Detection of forest pests based on improved YOLOv4[J]. Acta Agriculturae Zhejiangensis, 2022, 34(6): 1306-1315.

Figures/Tables 12

References 21

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害虫种类 Pest species	害虫尺寸 Pest size	样本数量 Sample quantity	害虫种类 Pest species	害虫尺寸 Pest size	样本数量 Sample quantity
美苔蛾Miltochrista miniata	小Small	1 060	斜矛丽金龟Callistethus plagiicollis	中Medium	1 057
黄土苔蛾Eilema nigripoda	小Small	1 067	铜绿丽金龟Anomala corpulenta Motschulsky	大Big	1 289
条纹小斑蛾Thyrassia penangae	小Small	1 057	毛黄鳃金龟Holotrichia trichophora	大Big	1 156
突背斑红蝽Physopelta gutta	小Small	1 189	茶斑蛾Eterusia aedea	大Big	1 081
八点灰灯蛾Creatonotos transiens	中Medium	1 060	蚱蜢Locusta migratoria	大Big	903

害虫种类 Pest species	害虫尺寸 Pest size	样本数量 Sample quantity	害虫种类 Pest species	害虫尺寸 Pest size	样本数量 Sample quantity
美苔蛾Miltochrista miniata	小Small	1 060	斜矛丽金龟Callistethus plagiicollis	中Medium	1 057
黄土苔蛾Eilema nigripoda	小Small	1 067	铜绿丽金龟Anomala corpulenta Motschulsky	大Big	1 289
条纹小斑蛾Thyrassia penangae	小Small	1 057	毛黄鳃金龟Holotrichia trichophora	大Big	1 156
突背斑红蝽Physopelta gutta	小Small	1 189	茶斑蛾Eterusia aedea	大Big	1 081
八点灰灯蛾Creatonotos transiens	中Medium	1 060	蚱蜢Locusta migratoria	大Big	903

模型Model	特征图Feature map
YOLOv4	13×13、26×26、52×52
Improved YOLOv4-1	13×13、26×26、52×52、104×104
Improved YOLOv4-2	26×26、52×52、104×104
Improved YOLOv4-3	52×52、104×104
Improved YOLOv4-4	104×104

模型Model	特征图Feature map
YOLOv4	13×13、26×26、52×52
Improved YOLOv4-1	13×13、26×26、52×52、104×104
Improved YOLOv4-2	26×26、52×52、104×104
Improved YOLOv4-3	52×52、104×104
Improved YOLOv4-4	104×104

模型 Model	MAP/%	FPS	FLOPs/B	MP/MB
YOLOv3	91.9	41	65	240
YOLOv4	94.8	36	59	250
Improved YOLOv4-1	96.1	30	69	254
Improved YOLOv4-2	96.3	33	64	187
Improved YOLOv4-3	96.0	38	58	171
Improved YOLOv4-4	96.4	40	52	167

Detection of forest pests based on improved YOLOv4

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编号 Number	害虫尺寸 Pest size	YOLOv3/%	YOLOv4/ %	Improved YOLOv4-4/ %
1	小Small	89.2	93.1	96.0
2	小Small	91.4	91.7	94.9
3	小Small	91.5	94.4	96.0
4	小Small	92.8	93.0	94.4
5	中Medium	97.1	98.3	98.0
6	中Medium	96.1	97.7	96.9
7	大Big	98.3	99.1	99.1
8	大Big	97.0	96.9	97.7
9	大Big	98.5	99.0	99.0
10	大Big	95.8	98.9	97.5
11	—	62.5	80.9	90.9
MAP	—	91.9	94.8	96.4

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