一种轻量级YOLOv5S农作物虫害目标检测模型  

A lightweight YOLOv5S crop pest target detection model

作　　者：郭小燕[1] 于帅卿 GUO Xiaoyan;YU Shuaiqing(College of Information Science and Technology,Gansu Agricultural University,Lanzhou 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：《南京农业大学学报》2024年第5期1009-1018,共10页Journal of Nanjing Agricultural University

基　　金：国家自然科学基金项目(32360437);甘肃农业大学青年导师基金(QAU-QDFC-2021-18);甘肃农业大学科技创新基金(盛彤笙创新基金)(GSAU-STS-2021-16)。

摘　　要：[目的]本文提出一种轻量级YOLOv5S农作物虫害目标检测模型以解决在样本数量不足的情况下农作物虫害识别的问题。[方法]利用Ghost技术将2个Ghost Bottle Block线性特征提取模块封装为1个GB模块,代替YOLOv5S中前7个CBL、CSP、SPP非线性特征提取模块,从而约简了YOLOv5S的网络参数,减轻了网络体量。[结果]在保证虫害检测效果的前提下降低网络对计算硬件与训练样本的依赖。为了验证模型的有效性,对水稻、玉米、棉花、马铃薯、苜蓿5种农作物的12类虫害进行识别与定位,多类别平均精度(mAP)为91.31%,比YOLOv5S模型高出2.56百分点。[结论]通过与SSD、Faster-RCNN、YOLOv5S模型对比发现,本文提出的模型在mAP、F1-score、精确率(Precision)、召回率(Recall)4个评价指标均占优势,尤其在小目标、密集目标、与背景相似目标的检测方面表现突出。[Objectives]A light weight YOLOv5S crop pest target detection model was proposed to solve the problem of identifying crop pests,when the sample quantity was insufficient.[Methods]Two Ghost Bottle Block linear feature extraction modules were encapsulated into one GB module using Ghost technology,replacing the first seven CBL,CSP and SPP nonlinear feature extraction modules in YOLOv5S,which reduced the parameters and the volume of YOLOv5S.[Results]The network’s dependence on computing hardware and training samples was reduced while ensuring the effectiveness of pest detection.In order to verify the effectiveness of the model,12 types of pests were identified and located for 5 crops,including rice,corn,cotton,potatoes and alfalfa.The multi category mean average precision(mAP)was 91.31%,which was 2.5 percentage points higher than the YOLOv5S model.[Conclusions]By comparing with SSD,Faster RCNN and YOLOv5S,it was found that the model proposed in this paper had advantages in mAP,F1 score,precision and recall evaluation indicators,especially in the detection of small targets,dense targets and targets similar to the background.

关 键 词：农作物 虫害 YOLOv5S 轻量级 目标检测 

分 类 号：S24[农业科学—农业电气化与自动化] TP39[农业科学—农业工程]