Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (12): 257-263.doi: 10.11896/jsjkx.221000203

• Computer Graphics & Multimedia • Previous Articles     Next Articles

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).

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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

CLC Number: 

  • 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.
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