改进YOLOv5识别复杂环境下棉花顶芽  

Cotton top bud recognition method based on YOLOv5-CPP in complex environment

作　　者：彭炫 周建平[1,2] 许燕 席光泽[1] PENG Xuan;ZHOU Jianping;XU Yan;XI Guangze(School of Mechanical Engineering,Xinjiang University,Urumqi 830017,China;Engineering Research Center of Agricultural and Animal Husbandry Robots and Intelligent Equipment,Xinjiang Uygur Autonomous Region,Urumqi 830017,China)

机构地区：[1]新疆大学机械工程学院,乌鲁木齐830017 [2]新疆维吾尔自治区农牧机器人及智能装备工程研究中心,乌鲁木齐830017

出　　处：《农业工程学报》2023年第16期191-197,共7页Transactions of the Chinese Society of Agricultural Engineering

基　　金：2022新疆大学优秀博士研究生科研创新项目(XJU2022BS086);新疆维吾尔自治区自然科学基金项目:复杂环境下棉花顶芽识别定位与激光去除技术研究(No.2022D01C67);新疆农机研发制造推广应用一体化项目:棉花激光打顶机研发制造推广应用(No.2022D14002)。

摘　　要：为提高复杂环境下棉花顶芽识别的精确率,提出了一种基于YOLOv5s的改进顶芽识别模型。建立了田间复杂环境下棉花顶芽数据集,在原有模型结构上增加目标检测层,提高了浅层与深层的特征融合率,避免信息丢失。同时加入CPP-CBAM注意力机制与SIOU边界框回归损失函数,进一步加快模型预测框回归,提升棉花顶芽检测准确率。将改进后的目标检测模型部署在Jetson nano开发板上,并使用TensorRT对检测模型加速,测试结果显示,改进后的模型对棉花顶芽识别平均准确率达到了92.8%。与Fast R-CNN、YOLOv3、YOLOv5s、YOLOv6等算法相比,平均准确率分别提升了2.1、3.3、2、2.4个百分点,该检测模型适用于复杂环境下棉花顶芽的精准识别,为后续棉花机械化精准打顶作业提供技术理论支持。Cotton bud is one of the most important cash crops with many uses,such as the textile and cotton fabric raw material.However,manual cotton topping cannot fully meet the large-scale production in recent years,due to the efficiency and labor costs.Intelligent mechanical topping can be expected to serve as an inevitable trend during cotton topping.There is a high demand to accurately identify the cotton top bud under the complex environment(such as light and shadow),particularly for the various shape characteristics of cotton top core in the process of cotton cap removal.In this study the accurate recognition was proposed to locate the cotton top bud in the complex environment using an improved YOLOv5s.A dataset was also constructed to contain 3103 cotton top buds with different morphological characteristics under a complex environment.Three categories were divided:single,multi-plant and screened top bud under different occlusion areas,weather and light conditions.Systematic training and analysis were then performed on the data.An improved YOLOv5s model was proposed to reduce the error and leakage detection for the sufficient features of small targets.Multi-scale target features were also effectively extracted using only three detection heads in the original YOLOv5s.At the same time,the ideal extraction of the improved YOLOv5s was obtained with the deepening of the network layer.The object detection and CPP-CBAM attention mechanism were first added to the original structure,in order to improve the shallow and deep feature fusion rate.Secondly,the regression of prediction frame was enhanced to avoid the information loss of the real frame.The loss function SIOU boundary frame regression was added to further accelerate the prediction frame regression speed for the detection accuracy.The SIOU loss function was introduced by the vector angle between the real and predicted frame including angle,distance,and shape loss function.As such,the aspect ratio between the predicted and real frame was considered in the CIOU loss fu

关 键 词：作物 图像识别 小目标检测 YOLOv5s SIOU损失函数 CPP-CBAM注意力机制 棉花打顶 

分 类 号：S24[农业科学—农业电气化与自动化]