基于改进YOLOv4的轻量化菠萝苗心检测算法  

Lightweight algorithm for pineapple plant center detection based on improved an YOLOv4 model

作　　者：张日红[1] 区建爽 李小敏 凌轩[1] 朱政 侯炳法 ZHANG Rihong;Ou Jianshuang;LI Xiaomin;LING Xuan;ZHU Zheng;HOU Bingfa(College of Mechanical and Electrical Engineering,Zhongkai University of Agriculture and Engineering,Guangzhou 510225)

机构地区：[1]仲恺农业工程学院机电工程学院,广州510225

出　　处：《农业工程学报》2023年第4期135-143,共9页Transactions of the Chinese Society of Agricultural Engineering

基　　金：“十四五”广东省农业科技创新十大主攻方向“揭榜挂帅”项目(2022SDZG03);2022年度广东省普通高校特色创新类项目(2022KTSCX057);2021年广东省科技创新战略专项资金项目(pdjh2021b0245)。

摘　　要：当前菠萝催花作业以人工喷洒为主,生产效率低、劳动强度大。菠萝苗心位置的精准识别和定位是实现机械化、智能化菠萝催花的核心问题。该研究在YOLOv4目标识别算法的基础上,选择GhostNet作为主干特征提取网络,构建了一种混合网络模型,并在颈部网络中融合深度可分离卷积与轻量级的注意力模块。改进后的模型相较于YOLOv4模型的总参数量减少70%。与YOLOv4、Faster R-CNN和CenterNet 3个模型进行检测对比试验,结果可得:改进模型在菠萝植株种植密集与稀疏的条件下识别精度分别为94.7%和95.5%,实时识别速度可达27帧/s,每张图像平均检测时间为72 ms,相比常规YOLOv4模型用时缩短23%。总体性能表现均优于对比组的目标检测模型。总的来说,改进模型YOLOv4-GHDW在一定程度上实现了检测速度、识别精度和模型体量三者之间平衡,能够在实际种植环境下对菠萝苗心有较好的识别效果。研究结果可为智能化菠萝精准催花设备研发提供视觉技术支持。Spraying is one of the main physiological and biochemical processes for flower induction in pineapple production.However,manual spraying cannot fully meet large-scale production in recent years,due to the very high labor intensity.Fortunately,automatic spraying can be expected for the flower induction of pineapple during this time.Among them,it is a high demand to accurately detect the center position of pineapple plants during spraying.In this study,a hybrid network model was proposed to accurate recognize the pineapple plant center using an improved YOLOv4 deep learning.The conventional YOLOv4 model shared an outstanding detect speed more suitable for the agricultural robot.However,the large number of parameters occupied a huge amount of memory in the original model,leading to more computing power in the device.Therefore,it was necessary for a much lighter and more flexible network structure in the reasoning work,particularly with the shorter running time in the actual field operation.The lightweight was then realized using the network framework of the YOLOv4 model.Firstly,the number of network layers and feature map channels in the backbone were reduced using the GhostNet network structure as the backbone to replace the CSP-Darknet53,which enhanced the efficiency of the feature extraction.The GhostNet presented a Ghost module for building efficient neural architectures,in order to reduce the computational costs of recent deep neural networks.As such,the Ghost bottlenecks performed better in the lightweight structures of the entire network.Secondly,the depthwise separable convolution was introduced into the Neck network.The convolution set was integrated to reduce the number of parameters with a high level of feature extraction.Besides,the lightweight attention mechanism(called CBAM)was added between the backbone and the neck.Attention weights were then put on the channel and spatial dimensions for a better connection in the channel and space,in order to extract the effective features of the target.Finally,the

关 键 词：机器视觉 图像处理 菠萝催花 目标检测 深度可分离卷积 GhostNet YOLOv4 

分 类 号：S24[农业科学—农业电气化与自动化]