首页|基于改进YOLOv8的自然环境下柑橘果实识别

基于改进YOLOv8的自然环境下柑橘果实识别

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为实现柑橘果实的精准快速识别,提出了一种改进YOLOv8网络模型.首先将YOLOv8网络模型中的部分传统卷积替换为ODConv全维动态卷积,以增强YOLOv8网络模型在复杂的自然环境下的适应能力,然后将YOLOv8的CIoU损失函数替换为MPDIoU损失函数,解决了CIoU损失函数在特殊情况下退化的问题,接着通过消融试验,分别验证了ODConv全维动态卷积与MPDIoU损失函数的有效性,改进后YOLOv8n、YOLOv8s、YOLOv8m、YOLOv8l、YOLOv8x的平均识别精度mAP分别从 86.40%、88.92%、88.97%、88.99%、89.11%提高至88.25%、89.32%、89.57%、89.90%、90.12%.试验结果表明,ODConv全维动态卷积与MPDIoU损失函数能有效提高YOLOv8网络模型在自然环境下的柑橘果实识别能力.
Citrus fruit recognition in natural environment based on improved YOLOv8
In order to achieve precise and fast identification of citrus fruits,an improved YOLOv8 was proposed.Firstly,certain tradi-tional convolutions in the YOLOv8 were replaced with ODConv full-dimensional dynamic convolutions to enhance the model's adapt-ability in complex natural environments.Subsequently,the CIoU loss function of YOLOv8 was substituted with the MPDIoU loss func-tion to address the degradation issue of the CIoU loss function in specific scenarios.Furthermore,the effectiveness of ODConv full-di-mensional dynamic convolutions and MPDIoU loss function was verified through a series of ablation experiments.The average recogni-tion accuracy(mAP)of the improved models,YOLOv8n,YOLOv8s,YOLOv8m,YOLOv8l,and YOLOv8x,was increased from 86.40%,88.92%,88.97%,88.99%,89.11%to 88.25%,89.32%,89.57%,89.90%,90.12%,respectively.Experimental results demonstrated that ODConv full-dimensional dynamic convolutions and MPDIoU loss function significantly enhanced the citrus fruit identification capability of the YOLOv8 in natural environments.

citrus fruit recognitionconvolutional neural networkYOLOv8ODConv full-dimensional dynamic convolutionMPDI-oU loss function

余圣新、韦莹莹、方辉、李敏、柴秀娟、曾志康、覃泽林

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广西壮族自治区农业科学院农业科技信息研究所,南宁 530000

中国农业科学院农业信息研究所,北京 100000

柑橘果实识别 卷积神经网络 YOLOv8 ODConv全维动态卷积 MPDIoU损失函数

广西创新驱动发展专项资金项目广西创新驱动发展专项资金项目广西壮族自治区农业科学院科技发展基金项目广西壮族自治区农业科学院稳定资助科研团队基金项目

桂科AA22036002桂科AA20108003桂农科2023JZ09桂农科2021YT077

2024

10.14088/j.cnki.issn0439-8114.2024.08.005

湖北农业科学
湖北省农业科学院　华中农业大学　长江大学　黄冈师范学院

湖北农业科学

CSTPCD
影响因子：0.442
ISSN：0439-8114
年,卷(期)：2024.63(8)
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