计算机科学

计算机科学 ›› 2025, Vol. 52 ›› Issue (11A): 250200043-7.doi: 10.11896/jsjkx.250200043

• 计算机图形学&多媒体 • 上一篇    下一篇

基于特征增强和群组混合注意力的棉花病害检测

王宏强, 赵晖, 贾振红   

  1. 新疆大学计算机科学与技术学院 乌鲁木齐 830049
  • 出版日期:2025-11-15 发布日期:2025-11-10
  • 通讯作者: 赵晖(zhaohui@xju.edu.cn)
  • 作者简介:zsebzy@stu.xju.edu.cn
  • 基金资助:
    自治区重点研发项目(2023B01032);国家重大科技专项课题(2022ZD0115802)

Cotton Disease Detection Based on Feature Enhancement and Group Mix Attention

WANG Hongqiang, ZHAO Hui, JIA Zhenhong   

  1. College of Computer Science and Technology,Xinjiang University,Urumqi 830049,China
  • Online:2025-11-15 Published:2025-11-10
  • Supported by:
    Key R&D Projects in the Autonomous Region(2023B01032) and National Science and Technology Major Project of the Ministry of Science and Technology of China(2022ZD0115802).

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摘要： 为了在田间真实环境中对棉花病害进行快速、准确检测,提出了一个基于特征增强和注意力机制的棉花病害目标检测模型。为确保模型在真实田间环境中检测的准确性,在Neck模块中,使用Group Mix Attention来联系上下文信息丰富特征图信息,捕获更多样化和细微的特征。使用特征增强模块对特征图进行加权处理,以降低背景或其他物体对图像中目标的干扰。所提模型使用了SIoU损失函数,能够有效提高真实田间环境中模型的检测精度,有效减少了模型误检和漏检的情况。实验结果表明,所提模型在自建真实田间环境棉花病害目标检测数据集上均表现出色,有效提高了检测精度。与基线模型相比,所提模型的mAP和Precision分别提升了2个百分点和4.5个百分点。

关键词: 棉花病害, 小目标, 特征增强, 注意力机制, 损失函数

Abstract: In order to achieve rapid and accurate detection of cotton diseases in real field environments,this paper proposes a cotton disease target detection model based on feature enhancement and attention mechanism.To ensure the accuracy of the model’sdetection in real field environments,an improved feature enhancement module is used in the Neck module to weight the feature maps and reduce the interference of background or other objects on the targets in the image.After the feature enhancement mo-dule,Group Mix Attention is used to connect contextual information and enrich the feature map information.The proposed model can effectively improve the detection accuracy of models in real field environments,effectively reducing the occurrence of model false positives and false negatives using SIoU loss function.The experimental results show that the proposed model performs well on the self built real field environment cotton disease target detection dataset,effectively improving the detection accuracy of the model in real field environments.Compared with the baseline model,the mAP and Precision have increased by 2 percentage points and 4.5 percentage points.

Key words: Cotton diseases and pests, Small goals, Feature enhancement, Attention mechanism, Loss function

中图分类号: 

  • TP391
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