基于优化靶向诱捕器的YOLO v8活体稻纵卷叶螟智能识别方法

YOLO v8-based Intelligent Recognition of Live Cnaphalocrocis medinalis Using Optimized Targeted Trapping Device

摘要:

针对水稻活体害虫智能识别过程中容易出现目标小、姿态各异以及诱捕时诱捕装置效率不高等问题，导致害虫识别精度不足，本文提出一种基于一拖三活体靶向诱捕器改进YOLO v8n模型的水稻害虫智能识别方法。通过改进传统靶向识别装置结构，设计一拖三活体靶向诱捕装置提高工作效率；针对智能识别所需数据集制作，提出图像逐帧清晰度对比排序法进行数据集提取，通过改进CBAM注意力机制引入自适应模块，提升模型对目标区域关注度，改进AFPN结构，在AFPN中增加高分辨率P2层，以增强对小目标的识别能力。模型试验结果表明，改进后的YOLO v8n模型在稻纵卷叶螟数据集上的平均精度均值（mAP@0.5）达到95.65%。消融试验结果表明，改进后的CBAM模块、引入P2层AFPN结构均显著提升模型性能，使模型综合识别能力达到最优。田间试验进一步验证改进后的YOLO v8n模型实际应用效果，结合靶向识别装备在稻田中稻纵卷叶螟识别率达90%以上，表现出较好鲁棒性和稳定性。综上，本文提出基于YOLO v8n改进的模型水稻活体害虫识别方法能够有效提高识别精度与效率，为水稻害虫精准识别和防控提供科学依据，具有较强实际应用价值与推广潜力。

Abstract:

Aiming to address the issues of small target size, varying pest postures, and low detection efficiency, and suboptimal performance of trapping devices during live rice pest identification, an intelligent recognition method was proposed based on an improved YOLO v8n model integrated with a one-to-three live targeted trapping device. By optimizing the structural design of the traditional targeted recognition equipment, a one-to-three live targeted trapping device was developed to enhance operational efficiency. For the construction of the dataset required for intelligent recognition, a frame-by-frame image clarity comparison and sorting method was proposed to improve data quality. Additionally, an adaptive module was introduced into the improved CBAM attention mechanism to enhance the model’s focus on target regions, and a high-resolution P2 layer was added to the AFPN structure to improve small target recognition capability. Comparative experiments demonstrated that the improved YOLO v8n model achieved a mean average precision (mAP@0.5) of 95.65% on the rice leaf folder dataset. Ablation experiments further verified that the enhanced CBAM module and the introduction of the P2 layer in the AFPN structure significantly improved model performance, resulting in optimal overall recognition capability. Field trials confirmed the practical effectiveness of the improved YOLO v8n model, achieving a recognition rate of over 90% for rice leaf folder pests across 60 mu (approximately 4 hectares) of rice fields when combined with the targeted identification device, demonstrating good robustness and stability. In summary, the intelligent recognition method for live rice pests based on YOLO v8n the improved model proposed effectively enhanced recognition accuracy and efficiency, providing a scientific basis for precise pest identification and control in rice fields, with strong practical application value and promotion potential.