A Lightweight Rice Pest Detection Algorithm Using Improved Attention Mechanism and YOLOv8

被引:0
|
作者
Yin, Jianjun [1 ,2 ]
Huang, Pengfei [1 ,2 ]
Xiao, Deqin [1 ,2 ]
Zhang, Bin [1 ,2 ]
机构
[1] South China Agr Univ, Coll Math & Informat, Guangzhou 510642, Peoples R China
[2] Minist Agr & Rural Affairs, Key Lab Smart Agr Technol Trop South China, Guangzhou 510642, Peoples R China
来源
AGRICULTURE-BASEL | 2024年 / 14卷 / 07期
关键词
pest detection; YOLOv8; attention mechanism; loss metric; lightweight model;
D O I
10.3390/agriculture14071052
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
Intelligent pest detection algorithms are capable of effectively detecting and recognizing agricultural pests, providing important recommendations for field pest control. However, existing recognition models have shortcomings such as poor accuracy or a large number of parameters. Therefore, this study proposes a lightweight and accurate rice pest detection algorithm based on improved YOLOv8. Firstly, a Multi-branch Convolutional Block Attention Module (M-CBAM) is constructed in the YOLOv8 network to enhance the feature extraction capability for pest targets, yielding better detection results. Secondly, the Minimum Points Distance Intersection over Union (MPDIoU) is introduced as a bounding box loss metric, enabling faster model convergence and improved detection results. Lastly, lightweight Ghost convolutional modules are utilized to significantly reduce model parameters while maintaining optimal detection performance. The experimental results demonstrate that the proposed method outperforms other detection models, with improvements observed in all evaluation metrics compared to the baseline model. On the test set, this method achieves a detection average precision of 95.8% and an F1-score of 94.6%, with a model parameter of 2.15 M, meeting the requirements of both accuracy and lightweightness. The efficacy of this approach is validated by the experimental findings, which provide specific solutions and technical references for intelligent pest detection.
引用
收藏
页数:17
相关论文
共 50 条
  • [31] YOLOv8-E: An Improved YOLOv8 Algorithm for Eggplant Disease Detection
    Huang, Yuxi
    Zhao, Hong
    Wang, Jie
    APPLIED SCIENCES-BASEL, 2024, 14 (18):
  • [32] Attention-Based Lightweight YOLOv8 Underwater Target Recognition Algorithm
    Cheng, Shun
    Wang, Zhiqian
    Liu, Shaojin
    Han, Yan
    Sun, Pengtao
    Li, Jianrong
    Sensors, 2024, 24 (23)
  • [33] A Lightweight underwater detector enhanced by Attention mechanism, GSConv and WIoU on YOLOv8
    Shaobin Cai
    Xiangkui Zhang
    Yuchang Mo
    Scientific Reports, 14 (1)
  • [34] Lightweight YOLOv8 for Wheat Head Detection
    Fang, Chen
    Yang, Xiang
    IEEE ACCESS, 2024, 12 : 66214 - 66222
  • [35] Fabric defect detection algorithm based on improved YOLOv8
    Chen, Chang
    Zhou, Qihong
    Li, Shujia
    Luo, Dong
    Tan, Gaochao
    TEXTILE RESEARCH JOURNAL, 2024,
  • [36] Improved YOLOv8 Urban Vehicle Target Detection Algorithm
    Xu, Degang
    Wang, Shuangchen
    Wang, Zaiqing
    Yin, Kedong
    Computer Engineering and Applications, 2024, 60 (18) : 136 - 146
  • [37] Blueberry flower detection algorithm based on improved YOLOv8
    Gai, Rongli
    Zhang, Huatian
    Guo, Zhibin
    Kong, Xiangzhou
    Qin, Shan
    2023 19TH INTERNATIONAL CONFERENCE ON MOBILITY, SENSING AND NETWORKING, MSN 2023, 2023, : 768 - 773
  • [38] Improved YOLOv8 Algorithm for Industrial Surface Defect Detection
    Su, Jia
    Jia, Ze
    Qin, Yichang
    Zhang, Jianyan
    Computer Engineering and Applications, 2024, 60 (14) : 187 - 196
  • [39] Research on improved YOLOv8 algorithm for insulator defect detection
    Lin Zhang
    Boqun Li
    Yang Cui
    Yushan Lai
    Jing Gao
    Journal of Real-Time Image Processing, 2024, 21
  • [40] YOLOv8-QR: An improved YOLOv8 model via attention mechanism for object detection of QR code defects
    Zhao, Lun
    Liu, Jie
    Ren, Yu
    Lin, Chunli
    Liu, Jiyuan
    Abbas, Zeshan
    Islam, Md Shafiqul
    Xiao, Gang
    COMPUTERS & ELECTRICAL ENGINEERING, 2024, 118