计算机科学 ›› 2022, Vol. 49 ›› Issue (12): 257-263.doi: 10.11896/jsjkx.221000203
朱香元1, 聂轰1, 周旭2
ZHU Xiang-yuan1, NIE Hong1, ZHOU Xu2
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%。
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[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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