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
  • [1] A lightweight rice pest detection algorithm based on improved YOLOv8
    Yong Zheng
    Weiheng Zheng
    Xia Du
    Scientific Reports, 14 (1)
  • [2] A lightweight YOLOv8 based on attention mechanism for mango pest and disease detection
    Wang, Jiao
    Wang, Junping
    JOURNAL OF REAL-TIME IMAGE PROCESSING, 2024, 21 (04)
  • [3] Helmet detection algorithm based on lightweight improved YOLOv8
    Maoli Wang
    Haitao Qiu
    Jiarui Wang
    Signal, Image and Video Processing, 2025, 19 (1)
  • [4] Improved Lightweight Military Aircraft Detection Algorithm of YOLOv8
    Liu, Li
    Zhang, Shuo
    Bai, Yu’ang
    Li, Yujian
    Zhang, Chuxia
    Computer Engineering and Applications, 2024, 60 (18) : 114 - 125
  • [5] Improved YOLOv8 Lightweight UAV Target Detection Algorithm
    Hu, Junfeng
    Li, Baicong
    Zhu, Hao
    Huang, Xiaowen
    Computer Engineering and Applications, 2024, 60 (08) : 182 - 191
  • [6] Improved Lightweight Bearing Defect Detection Algorithm of YOLOv8
    Yao, Jingli
    Cheng, Guang
    Wan, Fei
    Zhu, Deping
    Computer Engineering and Applications, 2024, 60 (21) : 205 - 214
  • [7] A lightweight algorithm for steel surface defect detection using improved YOLOv8
    Shuangbao Ma
    Xin Zhao
    Li Wan
    Yapeng Zhang
    Hongliang Gao
    Scientific Reports, 15 (1)
  • [8] Improved Infrared Road Object Detection Algorithm Based on Attention Mechanism in YOLOv8
    Luo, Zilong
    Tian, Ying
    IAENG International Journal of Computer Science, 2024, 51 (06) : 673 - 680
  • [9] A Lightweight Fire Detection Algorithm Based on the Improved YOLOv8 Model
    Ma, Shuangbao
    Li, Wennan
    Wan, Li
    Zhang, Guoqin
    APPLIED SCIENCES-BASEL, 2024, 14 (16):
  • [10] GLU-YOLOv8: An Improved Pest and Disease Target Detection Algorithm Based on YOLOv8
    Yue, Guangbo
    Liu, Yaqiu
    Niu, Tong
    Liu, Lina
    An, Limin
    Wang, Zhengyuan
    Duan, Mingyu
    Forests, 2024, 15 (09):