计算机科学 ›› 2022, Vol. 49 ›› Issue (12): 257-263.doi: 10.11896/jsjkx.221000203

• 计算机图形学&多媒体 • 上一篇    下一篇

基于TPH-YOLOv5和小样本学习的害虫识别方法

朱香元1, 聂轰1, 周旭2   

  1. 1 肇庆学院计算机科学与软件学院 广东 肇庆 526061
    2 湖南大学信息科学与工程学院 长沙 410082
  • 收稿日期:2022-10-25 修回日期:2022-11-27 发布日期:2022-12-14
  • 通讯作者: 周旭(zhxu@hnu.edu.cn)
  • 作者简介:(zxycs@zqu.edu.cn)
  • 基金资助:
    广东省普通高校重点领域专项(新一代信息技术)(2021ZDZX1028);中国高校产学研创新基金“新一代信息技术创新项目”(2021ITA02027)

Pest Identification Method Based on TPH-YOLOv5 Algorithm and Small Sample Learning

ZHU Xiang-yuan1, NIE Hong1, ZHOU Xu2   

  1. 1 School of Computer Science and Software,Zhaoqing University,Zhaoqing,Guangdong 526061,China
    2 College of Information Science and Engineering,Hunan University,Changsha 410082,China
  • Received:2022-10-25 Revised:2022-11-27 Published:2022-12-14
  • About author:ZHU Xiang-yuan,born in 1974,Ph.D,associate professor.Her main research interests include parallel computing,computer vision,and applications of deep learning.ZHOU Xu,born in 1983,Ph.D,associate professor.Her main research interests include big data mining and parallel computing.
  • Supported by:
    Special Projects in for Key Fields of Ordinary Colleges and Universities in Guangdong Province(New Generation Information Technology)(2021ZDZX1028)and “New Generation Information Technology Innovation Project” of the Industry-University-Research Innovation Fund for China University(2021ITA02027).

摘要: 深度卷积目标检测算法可自动识别农田害虫,实现对害虫的监测和预警,确保农业稳产、增产,在智慧农业中有着重要的应用。针对小目标害虫漏检率高和小样本害虫识别精度低的问题,首先,设计有针对性的小目标和小样本害虫数据增强方法,采用复制粘贴、裁剪、过采样等技术,保证样本规模及位置多样性特性,进而提升其对训练损失的贡献度;其次,构建基于微调的二阶段小样本学习策略,兼顾分阶段学习基类和新类害虫特征,确保在识别新类害虫的同时,不降低基类害虫的识别能力,以满足不断更新害虫数据的实际农业应用场景需求;最后,引入TPH-YOLOv5作为害虫识别算法。在28类害虫图像数据集上进行实验,结果表明,所提方法具有较高的学习效率和识别正确率,其精度、召回率、平均精度均值分别为87.6%,84.9%和85.7%。

关键词: 深度学习, 害虫识别, 注意力机制, 小样本学习, TPH-YOLOv5, 数据增强

Abstract: Pest identification-based deep convolutional object detection is an important application of smart agriculture,which performs pest monitoring and ensures stable agricultural production.To solve the problems of high missed detection rate of small pests and low precision of small samples,a pest identification method based on the TPH-YOLOv5 algorithm and small sample learning is proposed.First,data augmentation for small objects and small samples is designed.Through copy and pasting,cropping,and oversampling,the number of training samples increases and the pest locations are diversified,which improves the contribution to training loss.Second,a two-stage small sample learning strategy based on fine-tuning is constructed.By learning the characteristics of basic and new categories of pests in different stages,the recognition precision of basic categories will not decrease while identifying new pests,which is suitable for the actual agricultural application of continuously collecting pest data.Finally,TPH-YOLOv5 is introduced as the pest identification algorithm.Rigorous tests are conducted on the 28 categories of pest images.The results show that the proposed method achieves high learning efficiency and recognition accuracy,with precision,recall,and mean average precision(mAP) of 87.6%,84.9% and 85.7%,respectively.

Key words: Deep learning, Pest identification, Attention mechanism, Small sample learning, TPH-YOLOv5, Data augmentation

中图分类号: 

  • TP391
[1]LAWRENCE C N,MOATAZ A,MOHAMMED A.Recent advances in imageprocessing techniques for automated leaf pest and disease recognition - A review[J].Information Processing in Agriculture,2021,8(1):27-51.
[2]MISBAH P,NIVEDITA P,HITESHRI Y,et al.Artificial cognition for applications in smart agriculture:A comprehensive review[J].Artificial Intelligence in Agriculture,2020,4:81-95.
[3]ZHOU F Y,JIN L P,DONG J.Review of Convolutional Neural Network[J].Chinese Journal of Computers,2017,40(6):1229-1251.
[4]LIU L,WANG R,XIE C,et al.PestNet:An End-to-End Deep Learning Approach for Large-Scale Multi-Class Pest Detection and Classification[J].IEEE Access,2019,7:45301-45312.
[5]JIAO L,DONG S,ZHANG S,et al.AF-RCNN:An anchor-free convolutional neural network for multi-categories agricultural pest detection[J].Computers and Electronics in Agriculture,2020,174:105522.
[6]LIANG Y,QIU R Z,LI Z P,et al.Identification Method of Major Rice Pests Based on YOLO v5 and Multi-source Datasets[J].Transactions of the Chinese Society for Agricultural,2022,53(7):250-258.
[7]LI R,WANG R,ZHANG J,et al.An Effective Data Augmentation Strategy for CNN-Based Pest Localization and Recognition in the Field[J].IEEE Access,2019,7:160274-16028.
[8]LI R,WANG R,XIE C,et al.A coarse-to-fine network for aphid recognition and detection in the field[J].Biosystems Enginee-ring,2019,187:39-52.
[9]WANG F,WANG R,XIE C,et al.Fusing multi-scale context-aware information representation for automatic in-field pest detection and recognition[J].Computers and Electronics in Agriculture,2020,169:105222.
[10]LIU L,XIE C,WANG R,et al.Deep Learning Based Automatic Multiclass Wild Pest Monitoring Approach Using Hybrid Global and Local Activated Features[J].IEEE Transactions on Industrial Informatics,2021,17(11):7589-7598.
[11]YANG X,LUO Y,LI M,et al.Recognizing Pests in Field-Based Images by Combining Spatial and Channel Attention Mechanism[J].IEEE Access,2021,9:162448-162458.
[12]WU X,ZHAN C,LAI Y K,et al.IP102:A Large-Scale Benchmark Dataset for Insect Pest Recognition[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR).2019:8779-8788.
[13]LI Y,WANG H,DANG L M,et al.Crop pest recognition in na-tural scenes using convolutional neural networks[J].Computers and Electronics in Agriculture,2020,169:105174.
[14]LIU Y,LIU S,XU J,et al.Forest pest identification based on a new dataset and convolutional neural network model with enhancement strategy[J].Computers and Electronics in Agriculture,2022,192:106625.
[15]KANG B,LIU Z,WANG X,et al.Few-Shot Object Detection via Feature Reweighting[C]//2019 IEEE/CVF International Conference on Computer Vision(ICCV).2019:8419-8428.
[16]SUN B,LI B,CAI S,et al.FSCE:Few-Shot Object Detection via Contrastive Proposal Encoding[C]//2021 IEEE/CVF Confe-rence on Computer Vision and Pattern Recognition(CVPR).2021:7348-7358.
[17]TONG K ,WU Y.Deep learning-based detection from the perspective of small or tiny objects:A survey[J].Image and Vision Computing,2022,123:104471.
[18]BOCHKOVSKIY A,WANG C Y,IAO H.YOLOv4:Optimal Speed and Accuracy of Object Detection[J].arXiv:2004.10934,2020.
[19] ZHANG H,CISSE M,DAUPHIN Y N,et al.Mixup:Beyond empirical risk minimization[J].arXiv:1710.09412,2017.
[20]ZHU X,LYU S,WANG X,et al.TPH--YOLOv5:ImprovedYOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarios[C]//2021 IEEE/CVF International Conference on Computer Vision Workshops(ICCVW).2021:2778-2788.
[21]WANG Z,JIN L,WANG S, et al.Apple stem/calyx real-time recognition using YOLO-v5 algorithm for fruit automatic loa-ding system[J].Postharvest Biology and Technology,2022,185:111808.
[22]WOO S,PARK J,LEE J,et al.CBAM:Convolutional block attention module[C]//Proceedings of the European Conference on Computer Vision(ECCV).2018:3-19.
[23]SELVARAJU R R,COGSWELL M,DAS A,et al.Gradcam:Visual explanations from deep networks via gradient-based localization[C]//Proceedings of the IEEE Conference on Compu-ter Vision and Pattern Recognition(CVPR).2017:618-626.
[1] 汤凌韬, 王迪, 张鲁飞, 刘盛云.
基于安全多方计算和差分隐私的联邦学习方案
Federated Learning Scheme Based on Secure Multi-party Computation and Differential Privacy
计算机科学, 2022, 49(9): 297-305. https://doi.org/10.11896/jsjkx.210800108
[2] 周芳泉, 成卫青.
基于全局增强图神经网络的序列推荐
Sequence Recommendation Based on Global Enhanced Graph Neural Network
计算机科学, 2022, 49(9): 55-63. https://doi.org/10.11896/jsjkx.210700085
[3] 戴禹, 许林峰.
基于文本行匹配的跨图文本阅读方法
Cross-image Text Reading Method Based on Text Line Matching
计算机科学, 2022, 49(9): 139-145. https://doi.org/10.11896/jsjkx.220600032
[4] 周乐员, 张剑华, 袁甜甜, 陈胜勇.
多层注意力机制融合的序列到序列中国连续手语识别和翻译
Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion
计算机科学, 2022, 49(9): 155-161. https://doi.org/10.11896/jsjkx.210800026
[5] 徐涌鑫, 赵俊峰, 王亚沙, 谢冰, 杨恺.
时序知识图谱表示学习
Temporal Knowledge Graph Representation Learning
计算机科学, 2022, 49(9): 162-171. https://doi.org/10.11896/jsjkx.220500204
[6] 熊丽琴, 曹雷, 赖俊, 陈希亮.
基于值分解的多智能体深度强化学习综述
Overview of Multi-agent Deep Reinforcement Learning Based on Value Factorization
计算机科学, 2022, 49(9): 172-182. https://doi.org/10.11896/jsjkx.210800112
[7] 饶志双, 贾真, 张凡, 李天瑞.
基于Key-Value关联记忆网络的知识图谱问答方法
Key-Value Relational Memory Networks for Question Answering over Knowledge Graph
计算机科学, 2022, 49(9): 202-207. https://doi.org/10.11896/jsjkx.220300277
[8] 姜梦函, 李邵梅, 郑洪浩, 张建朋.
基于改进位置编码的谣言检测模型
Rumor Detection Model Based on Improved Position Embedding
计算机科学, 2022, 49(8): 330-335. https://doi.org/10.11896/jsjkx.210600046
[9] 朱承璋, 黄嘉儿, 肖亚龙, 王晗, 邹北骥.
基于注意力机制的医学影像深度哈希检索算法
Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism
计算机科学, 2022, 49(8): 113-119. https://doi.org/10.11896/jsjkx.210700153
[10] 孙奇, 吉根林, 张杰.
基于非局部注意力生成对抗网络的视频异常事件检测方法
Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection
计算机科学, 2022, 49(8): 172-177. https://doi.org/10.11896/jsjkx.210600061
[11] 闫佳丹, 贾彩燕.
基于双图神经网络信息融合的文本分类方法
Text Classification Method Based on Information Fusion of Dual-graph Neural Network
计算机科学, 2022, 49(8): 230-236. https://doi.org/10.11896/jsjkx.210600042
[12] 王剑, 彭雨琦, 赵宇斐, 杨健.
基于深度学习的社交网络舆情信息抽取方法综述
Survey of Social Network Public Opinion Information Extraction Based on Deep Learning
计算机科学, 2022, 49(8): 279-293. https://doi.org/10.11896/jsjkx.220300099
[13] 郝志荣, 陈龙, 黄嘉成.
面向文本分类的类别区分式通用对抗攻击方法
Class Discriminative Universal Adversarial Attack for Text Classification
计算机科学, 2022, 49(8): 323-329. https://doi.org/10.11896/jsjkx.220200077
[14] 汪鸣, 彭舰, 黄飞虎.
基于多时间尺度时空图网络的交通流量预测模型
Multi-time Scale Spatial-Temporal Graph Neural Network for Traffic Flow Prediction
计算机科学, 2022, 49(8): 40-48. https://doi.org/10.11896/jsjkx.220100188
[15] 金方焱, 王秀利.
融合RACNN和BiLSTM的金融领域事件隐式因果关系抽取
Implicit Causality Extraction of Financial Events Integrating RACNN and BiLSTM
计算机科学, 2022, 49(7): 179-186. https://doi.org/10.11896/jsjkx.210500190
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!