针对番茄病害检测中病害表征细微多样及边缘设备算力受限,导致检测精度与实时性难以兼顾的问题,基于YOLO11提出了一种集成混合局部通道注意力(MLCA)、迭代注意力特征融合(iAFF)与轻量化卷积(GhostConv)的协同番茄叶片病害检测框架MIG-YOLO11。在MIG-YOLOII架构中,MLCA模块可以强化模型对大目标全局特征的捕捉能力和复杂场景下的语义表达;iAFF模块可以增强浅层高分辨率特征与深层语义特征的迭代融合能力,提升小目标的检测精度;GhostConv模块可以在保证特征表达能力的同时降低计算量和模型参数,使网络在保持高精度的同时具备良好的实时推理性能。实验结果表明,改进后的MIG-YOLO11在番茄病害检测任务中表现优异,其检测平均精度达到94.5%,较基线模型提升约2.1个百分点;同时,模型参数量减少约8.3%,每秒十亿次浮点运算(GFLOPs)降低约1.6%。MIG-YOLO在兼顾检测精度与计算效率的同时,有效突破了轻量化模型部署的性能瓶颈,展现出在资源受限场景下的广泛应用潜力。
Abstract
In response to the problems of subtle and diverse disease symptoms in tomato disease detection and the limited computing power of edge devices,which make it difficult to balance detection accuracy and real-time performance,this paper proposes a collaborative tomato leaf disease detection framework MIG-YOLO11 based on YOLO11,integrating a mixed local channel attention（MLCA）module,an iterative attention feature fusion（iAFF）module,and a lightweight convolution（GhostConv）module.In the MIG-YOLO11 architecture,the MLCA module can enhance the model＇s ability to capture global features of large targets and semantic expression in complex scenarios;the iAFF module can improve the iterative fusion ability of shallow high-resolution features and deep semantic features,thereby enhancing the detection accuracy of small targets;the GhostConv module can reduce the computational load and model parameters while maintaining feature expression capabilities,enabling the network to maintain high accuracy while having good real-time inference performance.Experimental results show that the improved MIG-YOLO11 performs excellently in tomato disease detection tasks,with an average detection accuracy of 94.5％,an increase of approximately 2.1 percentage points compared to the baseline model;at the same time,the model parameters are reduced by about 8.3% ,and the GFLOPs are decreased by approximately 1.6% .MIG-YOLO11 effectively breaks through the performance bottleneck of lightweight model deployment while balancing detection accuracy and computational efficiency,demonstrating broad application potential in resource-constrained scenarios.
关键词
轻量化 /
目标检测 /
注意力机制 /
病害识别 /
边缘设备计算
Key words
light weight /
object detection /
attention mechanism /
disease recognition /
edge device computing
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基金
河北省省级科技计划资助(21370103D); 2023年度河北省高等学校科学研究项目(2C2023123); 河北省软件工程重点实验室项目(22567637H); 河北省软件工程重点实验室开放课题(KF2307); 河北省农业效据智能感知与应用技术创新中心开放课题(ADIC2024Y001.ADIC2024Y003.ADIC2025Y005)
