基于改进YOLOv4的林业害虫检测

doi:10.3969/j.issn.1004-1524.2022.06.21

浙江农业学报 ›› 2022, Vol. 34 ›› Issue (6): 1306-1315.DOI: 10.3969/j.issn.1004-1524.2022.06.21

基于改进YOLOv4的林业害虫检测

陈道怀¹(), 汪杭军²^,^*()

1.浙江农林大学信息工程学院,浙江杭州 311300
2.浙江农林大学暨阳学院工程技术学院,浙江诸暨 311800

收稿日期:2021-07-03 出版日期:2022-06-25 发布日期:2022-06-30
通讯作者: 汪杭军
作者简介:*汪杭军,E-mail: whj@zafu.edu.cn
陈道怀（1997—）,男,浙江长兴人,硕士研究生,主要从事图像处理与模式识别研究。E-mail: 1310451391@qq.com
基金资助:
浙江省基础公益研究计划(LGN19C140006);绍兴市科技计划(2018C20013)

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

摘要/Abstract

摘要：

为了提高林业害虫检测的准确性,提出一种基于YOLOv4的改进算法。首先,基于智能害虫捕捉装置拍摄的图像,制作害虫数据集,采用K-means算法对样本数据集的目标框进行聚类分析,基于DIoU-NMS算法实现对害虫的计数功能;然后,在模型的路径聚合网络（PANet）结构上增加特征融合和104×104层级特征检测图,以提升对小个体害虫的识别率;最后,根据模型检测效率和复杂度,调整模型中的尺度特征图组合,在保证检测准确度的基础上,提升检测效率,并精简模型。试验结果表明,改进的YOLOv4模型的平均识别精度比传统YOLOv4模型提高了1.6百分点,且对于小个体害虫的识别效果更好,模型复杂度和模型参数量分别减少了11.9%、33.2%,检测速度提升了11.1%,更适于应用部署。

关键词: 林业害虫, 害虫检测, 深度学习

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

中图分类号:

S431.9

陈道怀, 汪杭军. 基于改进YOLOv4的林业害虫检测[J]. 浙江农业学报, 2022, 34(6): 1306-1315.

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

图/表 12

参考文献 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

基于改进YOLOv4的林业害虫检测

Detection of forest pests based on improved YOLOv4

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参考文献 21

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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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