基于改进YOLOv8的棉花虫害检测算法  

Cotton pest detection algorithm based on improved YOLOv8

作　　者：刘润飞 LIU Runfei(School of Information Science and Technology,Gansu Agricultural University,Lanzhou Gansu 730070,China)

机构地区：[1]甘肃农业大学信息科学技术学院,甘肃兰州730070

出　　处：《农业工程》2024年第7期42-47,共6页AGRICULTURAL ENGINEERING

摘　　要：传统的棉花病虫害检测方法依赖于植物保护专家或经验丰富的农民,这种方法费时费力,并且对于人眼难以观察到的微小病变识别准确率较低。这对如何利用深度学习方法提高识别的准确率和效率,降低人力成本,并且能够对微小病变进行准确识别,提出了新的挑战。针对此问题,提出一种准确率高和实时性好的基于改进YOLOv8的棉花虫害检测算法。首先,使用DCNv3结构,替换YOLOv8 C2模块Bottleneck结构中的普通卷积,形成新的模块记为C2f-DCNv3;其次,在Head的最后一个C2f-DCNv3模块后面加入高效通道注意力,在提升模型精度的同时保持能够实现实时检测。在开源的CottonInsect棉田昆虫识别研究图像数据集上的试验结果表明,所改进方法的mAP为0.706,推理时间为0.6ms,模型大小5.7 MB,相较于原模型YOLOv8n,mAP提升3.0,推理时间提升0.1 ms,模型大小基本保持不变,实现了对于棉花虫害准确且高效的识别。Traditional methods for detecting cotton pests and diseases rely on plant protection experts or experienced farmers,which is time-consuming and laborious,and has low accuracy in identifying small lesions that are difficult to observe by the human eye.This poses new challenges on how to use deep learning methods to improve recognition accuracy and efficiency,reduce labor costs,and ac-curately identify small lesions.To address this issue,a cotton pest detection algorithm based on improved YOLOv8 with high accuracy and good real-time performance was proposed.Firstly,DCNv3 structure was used to replace ordinary convolution in Bottleneck struc-ture of YOLOv8 C2 module,forming a new module named C2f-DCNv3.Secondly,Gaussian Contextual Attention was added after the last C2f-DCNv3 module in the Head to improve accuracy of model while maintaining real-time detection capability.Experimental results on the open source CottonInspect cotton field insect recognition research image dataset showed that,the mAP of improved method was 0.706,the Inference Time was 0.6 ms,and the model size was 5.7 MB.Compared with original model YOLOv8n,the mAP was in-creased by 3.0,the Inference Time was increased by 0.1 ms,and model size remained basically unchanged,achieving accurate and effi-cient identification of cotton pests.

关 键 词：YOLOv8 棉花虫害 目标检测 可变形卷积 高效通道注意力 

分 类 号：S24[农业科学—农业电气化与自动化]