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

被引:2
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
  • [21] Ship Detection Algorithm Based on YOLOv5 Network Improved with Lightweight Convolution and Attention Mechanism
    Wang, Langyu
    Zhang, Yan
    Lin, Yahong
    Yan, Shuai
    Xu, Yuanyuan
    Sun, Bo
    ALGORITHMS, 2023, 16 (12)
  • [22] LAYN: Lightweight Multi-Scale Attention YOLOv8 Network for Small Object Detection
    Ma, Songzhe
    Lu, Huimin
    Liu, Jie
    Zhu, Yungang
    Sang, Pengcheng
    IEEE ACCESS, 2024, 12 : 29294 - 29307
  • [23] Remote sensing small object detection algorithm based on improved YOLOv8
    Peng, Yanfei
    Qian, Jiani
    Tu, Shiting
    Li, Pai
    2024 5TH INTERNATIONAL CONFERENCE ON COMPUTER ENGINEERING AND APPLICATION, ICCEA 2024, 2024, : 1273 - 1278
  • [24] Lightweight model for detecting lotus leaf diseases and pests using improved YOLOv8
    Liu, Zhong
    Lu, Ange
    Cui, Hao
    Liu, Jun
    Ma, Qiucheng
    Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering, 2024, 40 (19): : 168 - 176
  • [25] Improved YOLOv8 Viscose Filaments Detection Algorithm Based on Swin Transformer
    Han Xinru
    Cai Linmin
    Xiang Qing
    Ma Lei
    2024 IEEE 4TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND ARTIFICIAL INTELLIGENCE, SEAI 2024, 2024, : 107 - 111
  • [26] Small Object Detection Algorithm Based on Improved YOLOv8 for Remote Sensing
    Yi, Hao
    Liu, Bo
    Zhao, Bin
    Liu, Enhai
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2024, 17 : 1734 - 1747
  • [27] Detection of Liquid Retention on Pipette Tips in High-Throughput Liquid Handling Workstations Based on Improved YOLOv8 Algorithm with Attention Mechanism
    Yin, Yanpu
    Lei, Jiahui
    Tao, Wei
    ELECTRONICS, 2024, 13 (14)
  • [28] ADL-YOLOv8: A Field Crop Weed Detection Model Based on Improved YOLOv8
    Jia, Zhiyu
    Zhang, Ming
    Yuan, Chang
    Liu, Qinghua
    Liu, Hongrui
    Qiu, Xiulin
    Zhao, Weiguo
    Shi, Jinlong
    AGRONOMY-BASEL, 2024, 14 (10):
  • [29] Efficient Tobacco Pest Detection in Complex Environments Using an Enhanced YOLOv8 Model
    Sun, Daozong
    Zhang, Kai
    Zhong, Hongsheng
    Xie, Jiaxing
    Xue, Xiuyun
    Yan, Mali
    Wu, Weibin
    Li, Jiehao
    AGRICULTURE-BASEL, 2024, 14 (03):
  • [30] Seatbelt Detection Algorithm Improved with Lightweight Approach and Attention Mechanism
    Qiu, Liankui
    Rao, Jiankun
    Zhao, Xiangzhe
    APPLIED SCIENCES-BASEL, 2024, 14 (08):